{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook for Conversion from JPEGs to TFRecords with feature and labels. Notebook (1/4) in the End-to-End Scalable Deep Learning Pipeline on Hops.\n",
    "\n",
    "TinyImageNet dataset contains a training set of 100,000 images, a validation set of 10,000 images, and a test\n",
    "set of also 10,000 images. These images are sourced from 200 different classes of objects. The images are downscaled from the original ImageNet’s dataset size of 256x256 to 64x64. \n",
    "\n",
    "The two initial tasks before we can train a model on this dataset are:\n",
    "\n",
    "1. Group the images with their labels \n",
    "2. Convert the JPEGs into TFRecords\n",
    "\n",
    "When using large datasets, like ImageNet, dealing with JPEGs is not very efficient, nor compatible with all of the functionality in the Tensorflow framework. \n",
    "\n",
    "TFRecords is a binary format for representing features and labels in Tensoflow, using a binary format for a large dataset can have a huge impact on disk space and processing speed. In addition, TFRecords are easier to work with than working with the raw JPEGs. \n",
    "\n",
    "However, TFRecords is not very friendly to query and analyze, so we can also store the feature data as Hive tables in the feature store.\n",
    "\n",
    "![step1.png](../images/step1.png)\n",
    "\n",
    "This notebook read JPEGs and labels from:\n",
    "\n",
    "- hdfs:///Projects/ImageNet_EndToEnd_MLPipeline/tiny-imagenet/tiny-imagenet-200/train\n",
    "- hdfs:///Projects/ImageNet_EndToEnd_MLPipeline/tiny-imagenet/tiny-imagenet-200/val\n",
    "- hdfs:///Projects/ImageNet_EndToEnd_MLPipeline/tiny-imagenet/tiny-imagenet-200/test\n",
    "\n",
    "and dataset metadata from:\n",
    "\n",
    "- hdfs:///Projects/ImageNet_EndToEnd_MLPipeline/tiny-imagenet/tiny-imagenet-200/words.txt\n",
    "- hdfs:///Projects/ImageNet_EndToEnd_MLPipeline/tiny-imagenet/tiny-imagenet-200/val/val_annotations.txt\n",
    "\n",
    "The notebook outputs feature groups (Hive ORC tables):\n",
    "\n",
    "- train\n",
    "- test\n",
    "- val\n",
    "\n",
    "The notebook outputs the following feature store training datasets (TFRecord files):\n",
    "\n",
    "- train_dataset_tinyimagenet\n",
    "- test_dataset_tinyimagenet\n",
    "- val_dataset_tinyimagenet\n",
    "\n",
    "To run this notebook you must have installed matplotlib in addition to the libraries available in the base conda environment (python 3.6 recommended as 2.7 will be deprecated soon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Package Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting Spark application\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<tr><th>ID</th><th>YARN Application ID</th><th>Kind</th><th>State</th><th>Spark UI</th><th>Driver log</th><th>Current session?</th></tr><tr><td>1378</td><td>application_1553861944920_0373</td><td>pyspark</td><td>idle</td><td><a target=\"_blank\" href=\"http://hadoop33:8088/proxy/application_1553861944920_0373/\">Link</a></td><td><a target=\"_blank\" href=\"http://hadoop19:8042/node/containerlogs/container_e71_1553861944920_0373_01_000001/EndToEndV2__kimham00\">Link</a></td><td>✔</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SparkSession available as 'spark'.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import pydoop.hdfs as py_hdfs\n",
    "from hops import hdfs\n",
    "import numpy as np\n",
    "from hops import featurestore\n",
    "from pyspark.sql.functions import lit\n",
    "from functools import reduce\n",
    "from pyspark.sql import DataFrame, Row\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%local\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from hops import hdfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Constants"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "PROJECT_DIR = hdfs.project_path()\n",
    "SHARED_DATASET = \"hdfs://10.0.104.196:8020/Projects/ImageNet_EndToEnd_MLPipeline/tiny-imagenet/tiny-imagenet-200/\"\n",
    "TRAIN_DIR = SHARED_DATASET + \"train\"\n",
    "TEST_DIR = SHARED_DATASET + \"test\"\n",
    "VAL_DIR = SHARED_DATASET + \"val/images/\"\n",
    "ID_TO_CLASS_FILE = SHARED_DATASET + \"/words.txt\"\n",
    "VAL_LABELS_FILE = SHARED_DATASET + \"val/val_annotations.txt\"\n",
    "FILE_PATTERN = \"*.JPEG\"\n",
    "SIZES_FILE = PROJECT_DIR + \"Resources/sizes.txt\"\n",
    "TRAIN_FEATUREGROUP = \"train_tinyimagenet\"\n",
    "TRAIN_FEATUREGROUP_VERSION = 1\n",
    "TEST_FEATUREGROUP = \"test_tinyimagenet\"\n",
    "TEST_FEATUREGROUP_VERSION = 1\n",
    "VAL_FEATUREGROUP = \"val_tinyimagenet\"\n",
    "VAL_FEATUREGROUP_VERSION = 1\n",
    "TRAIN_DATASET = \"train_dataset_tinyimagenet\"\n",
    "TEST_DATASET = \"test_dataset_tinyimagenet\"\n",
    "VAL_DATASET = \"val_dataset_tinyimagenet\"\n",
    "HEIGHT = 64\n",
    "WIDTH = 64\n",
    "CHANNELS = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define constants in %%local environment as well, for plotting and data analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%local\n",
    "PROJECT_DIR = hdfs.project_path()\n",
    "SHARED_DATASET = \"hdfs://10.0.104.196:8020/Projects/ImageNet_EndToEnd_MLPipeline/tiny-imagenet/tiny-imagenet-200/\"\n",
    "TRAIN_DIR = SHARED_DATASET + \"train\"\n",
    "TEST_DIR = SHARED_DATASET + \"test\"\n",
    "VAL_DIR = SHARED_DATASET + \"val/images/\"\n",
    "ID_TO_CLASS_FILE = SHARED_DATASET + \"/words.txt\"\n",
    "VAL_LABELS_FILE = SHARED_DATASET + \"val/val_annotations.txt\"\n",
    "FILE_PATTERN = \"*.JPEG\"\n",
    "SIZES_FILE = PROJECT_DIR + \"Resources/sizes.txt\"\n",
    "TRAIN_FEATUREGROUP = \"train_tinyimagenet\"\n",
    "TRAIN_FEATUREGROUP_VERSION = 1\n",
    "TEST_FEATUREGROUP = \"test_tinyimagenet\"\n",
    "TEST_FEATUREGROUP_VERSION = 1\n",
    "VAL_FEATUREGROUP = \"val_tinyimagenet\"\n",
    "VAL_FEATUREGROUP_VERSION = 1\n",
    "TRAIN_DATASET = \"train_dataset_tinyimagenet\"\n",
    "TEST_DATASET = \"test_dataset_tinyimagenet\"\n",
    "VAL_DATASET = \"val_dataset_tinyimagenet\"\n",
    "HEIGHT = 64\n",
    "WIDTH = 64\n",
    "CHANNELS = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration\n",
    "\n",
    "We can inspect some sample images from the dataset. Since Tensorflow support HDFS as a data source, we can use Tensorflow for reading from HDFS and then plot the images with matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5k0xg5XLev1RaY725UtWU4LjA16Na1Nz30EO4+wc/BADMawJlY5nDqUdocl9NJvxFUw6T/Np8I/BsMmz9X9TyGgRe08kmdpiRjEf7r0svlJNUpl6vt8hY+N3WbaOwtX9d3He+UmJuPJ+Ch4UKn9WwUWvZnyUGp9OZlu9U9WwvlEvx2BsucPHva7rTbuxgCL1oERnM861U+d1Sda7l+Fgkl85I2m7HO3yYLlY2PsVGFXqeUrls/FlvLxOOzanuRHAyHgcHBwcHBwcHB4ezFGd06TxZsZUNVy8rlysJaW4GNRmXZsXOhw2u7vMdXHXF1kOwZAugpqTdSkU2fWlZR0bGxLcy8r4XLFqBNiUwwOJkwdZwVOD5ixg8fjdEG6MnJrzhWVioudJtrjhbE3JDhW3a5RBBwostmpqRKltRt54zvOZK24/Mj1X3B434M11x299hLAayJJ3IohHy6E+n5A+r1abv+ygrTj4zw99yfHwSDqcHtsq3FbwxEY1G45jQY3sdh+NZk70So9/+LCy2qDwRi91+/MW2oCdiwNuZmlqthuXdpF/bGROT6LRfr7EYmUwmTmC2aJNJMBZblS7+u1KpHMPS23fbk4iPl2x7oiRow+J71n5/jievcvjX44Mf/o8AmgYOERRx8iO8693XAwAefeRBAMA9P2Ji7rr1lM5MM5cUtZpqN/QuxZPbdwAAZue4n3M2nQcAOHCYLPcFF9Kq0tpn6IXIFGiPOa+obcYnG5fQeNfwLYqkSGm6E1NTZPDMwvO8c+h92VFgP77zO4wc3PhaXkMtKGLZCo6bqTle6/PPkZXfsHGtzpns/eQUr2f0KM/ZRxk//B4jHzfd8BYAwNg4owwT2jatCPo553JfT/zsR9i/X/cnpGwnleR1ZOSDveMFSoGWr6AEqLvQgfki71t3h2xHFYU3GceBw6wFcFQ+/E8+/RQOHmKUo0dWoS/vd2PL6ULTeEQyEZtbHGfb5ljSbnvZHGNOZEvcVErwmTA5zPF89tujn+2viUTiFW2j26U5NURxZCLQfKad/bdIgjHySdlspnPZ5v7MjMWmfFKPpC35VzabgeebiiceU0z54cenbuOiRX0bKMnmfKHM52TL+ZQHJhV1MA/9opLwp6enMSZZ0riSha3+wYngmH0HBwcHBwcHBweHsxRnlNmfsaqVSlDoXUZd33x5HjNTpFfyHa2s2ZLl1ONGVTIoxuJlcwVM+3yvXFPSrvS8kdh5r2aVyvR3i6VU6yq1yeQZ+8gVnR94TUY/bGXJLQk3ROtrFEVIBunFm8b2nxZRsGpqnn/saqy5Sj6RtZ/+jleoUZykHMWVeXWOlnisfdh2vgf48YWJTa3quGm7F8mW79TrIRYWuFqdnKZedHzSafZPF6xwhyX2GFtRLpePqbzczui3V3f1PO+EzH47e23PWDKZjBmadq15O1MdF88pl4/Z34kSoRYz4Hv27GnZf172gbatRTesAuPiVzvf9mQw2799bjr9RCIRn3fMzJ4gWbk9YrIY7XkRJ9Pjn2w/Dv96RIp67T1MGnrZANnhjmwa//jf7wcQ1xDEpvPOAQAU5/h8/ftfvQ4A8OJOtsHly5cilSYrf9s3mdy7fTuTYtX9xc/F5VtZPXbl0FIs30Cd+3fv+RE3WjbE/e6ixj6vfWZ1rgeGj2LtUrL0aZ/72zlKxrshK84165h8O5tim54vTqNXLoCdGemfpX9P1PmMVBRBn59g3zw/xXFxZHgS25/guLp+iGzgww/SPnOEdcewbz+r/iYT7D8uunQDkhmef0n5WlXZY/sy1RhXaOShx34GAOjOd+HxJ3i/BgaYiDtyhFEBG1fnimQyPd8MI5JYsow5ATZmTcu+0+E04Jj8rdg3/Jh5RkLzpsA8QNq0/Am/addpbHWT9W/d1/DIcNtxjx1D2iPZJ7MwPlGfv7g/t6Ta5txOuQEWRa5Kyx/PmczwBXFSZ2A5Xik+F1ah2/5OpJuf23mfcw77mchUFvHObH7XzENbUJXqohQu99/PwnKWDJ+R2sLTeYQNIJC9++Lk6pPBMfsODg4ODg4ODg4OZynOKLM/K6Y4G/E1KtBWb7pcxZFRMg1pGY94Da5w1gbUDhppFqSUnZztRCpFdq9R5oqmUjGdOldsyUV8NsCVlY827dUi3TvQLFt8PPun5rbG4Gn16B2ra/a1emvUWleghiBoLbTQtFesLWL2jcI/PmO4OCveioXVZFfWaIs6xCx+nKUOeGbdqWiDOSFB2v35WTJElbrspbIRyll+v7hgq+HjnprDKcDxNPQAGRBjJRJtBTtOxPQvZlJOZLV5PNecdsvIV9LhH8/155X0/kEQxPkh7Rp3Y97bWR77O5/Px9rPE0UfDItZIDsXu3/tGv32+xqG4THsv0UMDO2F8RYf50T3wOHUwJNrzaq1GwAACfWZjz24C1m52L1WDjPf+tZtAIC3v/XNAIBQkc2hIbLoPupYJsZ96TL2iQcPso3l9JMfHR7jfzZTY/+Te+5BbtsTAIBMN6MKIuMx1E+XmmKRYQFrU709vZiYI3u9dhUdfGbLHAf3H+bYluxhO7zjR98DAFx9+VYER9kvd8taszbOsbIyyv0H0tInqzzZoR56jBbHhnHOavbljz/IHIGXX+JJZhUteOEFMvJXXEXLzy1bN+ORR3bzelLKb5FuuOZxDEgn+eVnd1C731noweH9dDEaWk6/02Z0TeOd7vnsPK9/dm4SftLTMRktec973gOH04O4EKj+9lu6JWnc4/mFVA5WeBR1/W25kI143qRamzCndM+UBXqNi2o1qqhVVfSwzvZk/XgUe5ir71XhzmQi3dyf5nSmkPC9Zi4n0HR8DL0QFe0/neWz3NXJ53Ogn6/m5mbR8CbjX2mOR2gdT20bcyMsqgBcuVZFWTa59/30AQBNxUmsPIlaxzA/SBxTfCzf0aW/lWdm46/yVINkCgkx+z9vwUbH7Ds4ODg4ODg4ODicpTijzH6tQP1eLcsV1QM/Y/GMRN3Di5LsJgMyG1mdWb3E1V69ztXQtLS683NlRCBbUJL+L6kVnJU9DmAFEPh+Kp9CSqxH3Yh286OP5fj8T0MrqUpYbzKrXqv3d3Ot1Orf2oii2NPV9uuFrauvpuNOq745FSSaUQD7smmd41WrnasuIWzEJZrNP9c0ZolAWraavGzF2nNlr3MJW88/l+d9rUrLNqdzTVQaSCgaU61w2yOH4XCaYL+HucsYi5BMJmN9o7lbmMbd2qYxD4vZ7XZmup3FttfFjPViB6Djvdq+FjveHOMwdRJnG9vO1zZ2jcayLFmypOU7lstj+QydnZ3xOdh3bBs7p/ach8VuPBY5sGu2c23fRyKRaHouv4Je9GQ1B/6/FINx+PlRqym6KweMTRvIZiO9CyXp7JcPkWX+7rfvAwC8970ch8wRpqLiUx0dXfDVb3YW2HYqC8oLoz093vWeawEAb33L6wEAX/rCCMam6YIzpVyAZI59ZUqP4IzYvxtvuQUA8NLzz6G/n9vsP8ABsLeHLPkvvvMmXled39m2/XEAQNa/CF0ht+lpcJxLSto+mKDPfyQz8K6I+x5coojFQg/e/zcfAwBMTvJae/ros7/38E4AQMPn+LtbNQcmEzWcu4W5dbt28j51FJQ/o3ytzrz+1jNZmh+Dn+DzNDqm8Vp92OZzqWPet4+5Fb0DZFW9IInN55LRv/DC83n+Pbweh1OPtDTn1s8lJMSvlctISV6RUbS1UmYfWcjRWcn6zFSyU6+ZuG+0GkQLxdbiU1kV5ixX2UYq9QoaYdu8xtQQym9s2CRNz08DjdiVMfawN7bcCoG1Mf0RGrHTULnOA1kdhyPDBwEcy4jbmJdKBvG4GtZanYOsHzd3oazqY1RK1XhsqpSV26I5Z7vjY1VzMtTCOBLhqd6T5YNaJMSuz/qoMAzj4qe+5YxFJy/Y6Jh9BwcHBwcHBwcHh7MUZ5TZzyznqn5eTPKFF2wGADz2k7uxhMYF2PYUX2+6liuaPbtZTnzFCvpwi2xGqRTBirdWqmIXjWVMamUYclUZs2mNusnAEFl5YmnA6qoKiForUxlFTa2u2Zg23Te0rzamNAgCoG2RdSJGr/27QLPqoDkXxHL7eGkWtrwfRdEihlUrT6vYa1V+Q6tWp8v2vdidKKmldVaC1LqYynKRq+ZAtQ66O/sQdJOJ6SiQxcpmxo97XQ7/enR3k3Uz1sRYhvn5+WOcc2yb9gq0i3EidrldT24s3PG2bf9Oe17B8Y7X/vfxqh02olYmw7a1c2mvqGsMy/j4ePys2nfbIxgWsTAs1tIbM9Pu52/7Wly5145jzE87I3S86zvRvXY4tfid3/5dAMBf/tc/BwA8uY2adISAuiyMjZJl/oV33giAbQcAdu6m20x3Bxnz3t5ulEpsXxvXMRqwfy+r7a5fT/391VdcDACIlB+WyCVQGWMbmi+qndTYf+byZEDPWcsx7KXnOMitXrEcr7r6MgDAg/fdCwAYHd4HAFhSuFTfJbs9J7ucIy/sx+VXUk/vl9hP/8obf4nn38PIuZeWDr+TfUNR53HFlo2I5thmVw5wwB2bokvOuqE1PE5JNXCG+Pndd9+HD/0B7221Rvbyc5/9PP8ukf3ff4D5C0NLefxKsYFySZFktf8ly+m0s+0p3sfVqxhtqOsZuvDii5CRa8pauRo99tgjcDg9sAhqQwqEhlj1WrWMpJjwQLmVDSkmAmn3+7sZMVo+yLaZSqUQyG8+1JTyyDCjQKUi+28JDpBUXkYNAUwR4WtilTTmW+x2UnmNtu9UIomqchKrZXNelIOP/OhT8sgPFRVYKC3AT5mao83Xv2Ye+q21VfzAcjE9TE+zj2jq+Hkd1o0vFMnix5XYwwiVCjfq7bNq2/bd5pwSWJwXlkDNNPtVi4RwrI/z37j3OOclDAFEFlHWNk6z7+Dg4ODg4ODg4PBvE2eU2X9sJ9mJyWGy9V/92k8AAN/8/MfxxU9/HADgJ7my+fJXufL8Tx+Utqwud45SqNc6ShVpZ6XFQtKcSKD3uaJKaYUY1euom6e8VnlN5s1ca/hXvWarsCheeXpRKzNqOipLbQ8sWz0AwnqrY09srGMVHsPjM6ZAc4UZe+Ub84pWxjJ28GnU45WsLe78NgYxrihnOjEPqGmt15DX8csvk63v7OTqOIh4HrMz/C1e/tkzmNQ5jE9SK77gbPZPGyZVGc9YhcFBOoTMzs7GTIKxA/a3aSRNy7gYx3POWQxjqu27nucdozlvd7g5nv7+52WvF++zWCy1XId9Zmx6LkfW1aIddk+mpqbiaz7RdR0vOnEid5ymK1a95bjJZPKYio/2eiInpMUOPu2vDqcWKfVln/zEXwAA7rrzWwCAtetW4DOf+WtuI/avKoe06Xm2Octvsi67Wq3i8kvJrCel/V29lKz8+vWMTl90CZn9T3yKkYRSvYxG7DzC/fR1s+0szLOv9GVVEsrlzO8pYNdTT/L/sxTer+/jM944yihEQWz5r97wJgDA5JEjCGZ5Tk8/wgjB0Epq3bMRo1QF6f4tuasrSwa2kB2MKwIfHqPDTu8Sfrb/JbL0l1x3AQBg7Kg09bk8EhG/c+klVwMAlv0J78Xffe4fAQBPbmPu3ZTGhABpTIyT8VwyyOf15b0c89cpujE6zohCvzT7mXQOlypa8r27vg8AqNaUbOFwyhEz+lYx3NpuBERSARidnE2qHSk3Y8VyRmVWr5bTUjbfHG+kIy+L8Z4YHwUApORHPznPMa1aryA2KBRDXbM5V+zjr1erUgsvZu4DnVO5yuNOTM3p+FIrmNthwkOP6e7N5x7Z+FqBZp9s/XulpPpNC2VkpLJIqfZQKmH5WnLFifMnrS4TkM7yHoyMM7oBq1ek5zERpPS3zjHpI5u2cVbzXen9a5aLaekLNtZ4zXHW8kGDV/DZP6OT/TvuZ2i1WxGPgtrU+37vo7jxfHYCuS5mfN7M/CS8sFNFPlSQZHqmqNcSyvPsDBLaj2c3TBNpL/bX1A9UKaOmyX4tbB2oPSiJz/5uamSaybs2+VZvHplDVFygS4fz/NgWM34Tx598H2ON6IVoNEwmZDIevUobZH83Qntg63EyS0KJUUFKA5gaZ6jwU6g4VKUeoiobp4aeqg2LkjheAAAgAElEQVTqiOsaDOfminbh3GeUQb3OAbKifrjiJvunDVaQxgpIdXQwfFoul2MpiXWyJkNZnCjbjldKEm2XvSy20Wzfx4ksJRdPnk8kYTleAasT2Wa2v7YnE+fz+fhaLRG3fcLeftzFk/BpJVXavbX7amFuk0ctXbo03t+wQtT2Wfs5Lj7eiRKaHU4tPvafPwwAWL16FQCgUmUS3sZ1y/D2t78GAHB0hAl5L+6kveTYGCecnd2U5tx0PYtrJSMPP72XtnlzKvZ4cD+LXT34wx8DAO7fsBEAkNYAu//QERS0mLjs0vMAAKU5ShjWXnARAODcjVwoHNzLMe38Ledi5ADPKbNqDQDg0vOZnFpWkl/GFteSL+x7eQLffp7Juu96x7sAAJ4mDePjzB4uSDY0Os5+w0+ybxhclkfvACdoCUk0fMk+O2VduvsJTtx7lDi8fvlSjI1xIlXUs9KT52fv/7VfBwBc+GkujPbs5rVccPlVqM1xgHj8CRba+ou/+CQA4KFHKc1ZuYpjzdGjepayGUxM8Pyfe44Wnrlcq7Wtw6lDnCyqeUlaRGkY1ZBWRnlesqq8EnaX9HNhtnYlJVlLlvK5KeQ7cXSUz5KvYqK5rMlrJMnR3+l5HTeZOuEcyKTINiRY4mmlBvhJTWzV15vkxubxvhYtqawm55kMpmbYrhJaIFgfH0tPYzK13rLPRBqoSoJTKPD8u/toiWljTeC1iWO8EJGIh7pOKpXMthzPyKn5WfZRxeIcTGQTE9P6XWpazNj4kVGRrWQq1ZTQejY2HiulXQwn43FwcHBwcHBwcHA4S3FGmf3cCrIu0xOU85SUyJTzgFfdTAuzu/6ZyT+qj4U1gyoXPkembVZs/vxCETVtlDYHozjxQjS2EiVSGV5mrV6Pt2lExoQrkdVWhrbqM4sn+M3wixJZGzh+gq5JdXzPj2n/+KP2VytoZVEBe428WJJjiRwm32lGB1rtQn14CI8pDmZsI1r+jpOL0YgjE5bgMT05o/vEfczN8p5P6kInqnVMhZYgw++mks5K8HSjPVk1lUrF7dYYfWML2otBnYxxPxFL326VeTIcr1BW+/5PJBdazHabXKb9nOxcLGIxOjp6zD7br9me8RMVAjte1KO9cJlFTGwftVotPu/2wl7t17k4StGeWOysN08PBhUu3vEUo8emKnj55c8iS3ULLryEjHskac6+Q2SV3/AaWmH29iihbnYe11z1KgDAxz5Cq0pfTWaMQxc2rVa7Fwv5y7e8Gf09lKz0dpNZnxmnZKGnk0z4wT1k9DctoVQnmJ7DeSvXAAAykoD6GhOLw2TRFxTO3y6GvJDrxJvf8Mu81ueYWLz3ICU5S5fz/He88BKAZqLwiiEWppyZDPHMc98F0Cya1dlLtnZITPuRUUYwkrpnF6xeh7EO9jsdsl32E2Q3e7K83orsf7duZrRjdO8eFDq5zeZNLHL23bvuAADc86MfAQDe9gtMKn7nO98CALjm1dfgD/+Q0Zmly3j/5uctsuxwqmGKjzAurCmW2AcSViBL29gYX8iR3Tbr1ayiAb2deYyPtsqBChYVkCVl3Rh3Mf1oeMcw+03L5tb+1Pr3arUa98fz82TFU1IwLFvGKHhnJ9uO7btYKSIvWXJNSomqvHgtqivn9lhxLaUQslmgo5PX2q/CeKtl32tR91SyeW4AUCtXUNXE6qlnmYxetkTgqhXOkolGigfsyeTiSEFG0eK1q9fwOmf47FmBUyu6hTBqWj7XWos5ngiO2XdwcHBwcHBwcHA4S3FGmf3DU1xR5ROkDYZWiombHkffIHVgHT1cmcGKL2jVklIxqIaxkF4A37fCQfyKlW82RtyE6sZg1+tV1MX6RZ5pvVpti0KtjrzItPx+nAPgxxovY8e1j3ors9/wG3GhA8Ss3/HvyfE0vE29fyv7jzg3wFh6SwgMYadWUSlziz7U4+9awR+xklESoRJzrbiFrSI7ulRGupuJLSUlTw5Xaqhq21yWLE9v/8DxL8zhXw3TsFrS6siINMadncdEaow5NivOdrZksd2koZ3pPl7RrROx9CcrkPVKOv/jRRbaC2LFdreWPCk2xCIY9t3Y8gzHRijar9fuUWqR3tHsTG3/hvZE4aNHj7ZcI9CMqrSz98crX96+jcOpxfrVZN7e/MbrAQDniGX+yP/xMVxyBZNOO8W8T89Sy3748BEAQDZLFnp6mv3f0t5+zCjZdLUsI6+/mkW0Esqn6pI1Xpf08blMGg2FE/Y+swsAcP+99wAArrqCmvb+fvarWTHus7PjWDjCZ7q3m4mydRXiqsyynU+OsA+YHSX9eM7lFyKTY7T7tjv+AQDQ0cNn5OIrLwcA5HO8zrDB91/ayQjAgw88ijCS5WKF17p/JyNlkdksq33uG2XCbq6jigUxkyNH+WyOTfJZueVNb+R5fPXrAID77n8QAHDhJZdiaCWLmt14i/IljjBB97JL+Ft89I8/BAD4049/GgAwPTeNwUHdS0X5zKDA4dTD5gGxkYi9hAAiMz5hGwxVsM58JwMpC6pltqF8dijW/tucq6PA37Cvh+35yATtWTMZFd+qNVApsx1VZQ1rggxLtRRpHmvog8CLC2/FCauBseX8clnt2kMQvx9pnpNUtKEjz2jWwCBfTfNe6OAzvURJ8r093Zidptqhs4tz1uUqUNfby2fZ1/xqdo5RgrnpOZTUD1x8GfNvxib5DI+P0MazpEKxlkScTqeRMLWIbE6f3PZQy83wdT2FLO/r4OAgBjT3yqbMctoV1XJwcHBwcHBwcHD4N4kzyuzXk1yV1MDVy5FJrnQ6S8BdPyALkhbTFtXI7CekYWrX0AKIi0LFTDhsFWSVo/jS0Ionqtfga9XqS5iWjL2T+H5Nq8zQNGxBMq7nnJB1Wsy0h60MnjHtXoSY5W+izYrzJGgyo21OPXaVx2Ew28tH1/TdhlatQZvTSsKPUIM0bIqemCNJrcr3p6Thnxazkx7sRa8Yzxk59RzYP/yK1+PwP4flYhW3bt0KAHjgATqEZLPZuM2ZI4zBXGTiMts/h2b/RHaQURT93Iy+wff9YyIEx9P1A61a+3ZLz3Z23D63CMBi205j+9vzFNqjHou/064Hteem3dnHYBEHoBk9icvG6zdod+dZXOzule6bw78OPXn+xv1d/N3uuvObAIChoc7YPi9Qnz8jK8zrbmBxrWVDZKEDRWhXDyzF777vtwEAX/vCFwEAHbLL2/08dbhPPfAYACDVQXatr7cPT21/AgBw6ACZ9Nedx4JZHb7GPdkDLqjvTGfSGDlKxnNWVpWdHWQbN6ylneaLz3Nfo1Nk9u+571Hc/1+/DAC4/Lot3E+an33mc18AAFx28SU8px72H7tf5D5efOF5XHAh8+YefIC21w/dT+vPZcojMLb1jbfSDq9vEKiKBb79h9T7/+1XbgcA3Hc3LTK78mQ93/ueXwUAvLx3H9asZg7ACzueBgBcfPlVPP8fU7N/663M0duy9VwAwB999I8xNy+7xgndi85m1M7h1MLyShYWFOnXHCqsl5GxIleaHRprH2kelZENZV1OMcmEH7P8oaw3zUlpyQAjbhMzjNJk5dbjZTwk02YdzmerLMbbmH7PJnAWDPWaUR+zr6xUOd4tlORsU1Vem9ylkqkgtmBX/TvUPStMxeNXNH6MjnEuc2DvPn438JGzyLGKsCYWRYf5PsenebXdhbkiyoo6JDu47bxySy3PUaZA6CzwP52dncjLySvQfHTT5jU8TkI5pBpCLQrR192DPrkjheq3pqdPHglzzL6Dg4ODg4ODg4PDWYozyuznO7jKq0xTp4gMWfxkMI2DR8nyb+qifnKmzFXW3t303e/pJ3uwsMAVXFSJ4MtxIWlaryTfqCpDG/JnjYsn+PU4Cz0tJ42kpZ5rG8sRqGnFlkgCvlZXDauPFZcwjisq8N/I3vdi9j+OOVg+gV4bbQRfzDpGgG85AbEJT5svudUNaFiBsHpTeycW3pN9hBcvCbUr3YDQAyKtCC1PYWaCzFOX6sv3dPLVXGCmSzVUbHWs4wXJVgbU4dShWCQVMDBAl41Kpek/bIx3VkyHscnGcheVuxGh6XhjblLtzjPtOvLFTPjPU6DK9m+vdm4WhTO23I5nLjbGqieTQTOXRA4PlYq54Mi5IGj9TkNtt1SsxOXJmzpN87bXeQStkYtKpRo/QxbJazLvcnFQmXdj+Ds6Oo5x+2mPUNi29n4YhsfkAjif/dODvqFlAIDHniZT/eg2MvAv7wEWKjsAAIePsJ3ceOOFAICRvWS8L37/bwEArr343QCAtcuAX/3FXwAA9KQY7ZwfI2v2+H1k9NM1/o7PvUTmOnfe+VjaQz1vQaxjQbr+u75LBnzLhfTZz3eRVX3i6Z/i37/3vQCAYY1/t932HQDAL76dbjW79tK7fuU6fvfKq1+NwfU85rpNdNnZuJnXPjlJ/f2T2+hTv/8w80zerH099Oif4KabXwcAeEm1Bt7//l8HAHz9X24DAHQUmDuwazc19udseTW+/E1+dvPr6et/5CDH5htecysA4EtfZPTj6lczX2J4ZBwf//gnAABr1vIct23bBgC4/bu8vi99+Z8BAMUF6pnf9Nob8NVvUPtvLjCZ1Mk1yA7/8+gfZNtMzWqOpCKjYS2BnKke5KCT0WtCHvedHRxzZlULAl4NJTHrNRWb6uxjO+rtY/5I7gi/s//APmhnSKm/zKmGUkrPzXyxtZ+1eVe9VkVtVue5OMcAiHMW01JXNNSvV8t1FPKKJqgokOn7bbxIq7/O5/hcGmufTCZRknPPnNwgFxaUM9bmWGRGOF4EyOwLc7N8s6A01GUD+Zb9G4IgQFp1CPLS5FttnS45YGWS/Nys9IMskFfkKx6PSiePGjtm38HBwcHBwcHBweEsxZnV7E/Li3RB7HYXVzrTxWns2Ue2f+1magrTKmlcSHO1MjlrHuNc2tRqDURaWA5KVtWVkt5f5YmR4nHKda46MwkvzoBOaElm+rREQrpbsfhF+SdHfiKumGumO6aPbxjjLrY8EdmKOIiZdvOwD8H91sWuhsbwx85B8hWPgFAVbM0wyLM8g9hYX1pkrUwjP4qZ+0Sy6dcPoFklV0vgcmTOK434OtLSiSVVbRdazQYq3dyhUtfZMMRsWStm6fdmG00ts8OpRU1t6OvfoP7Y8kgKHV2YmWE+hbXNktjtINFaAbZb2kzf91HVb1cTi1OVRtJY9c5OsjBFeRgHQTLWEJ7IK99yUBZr7O3/i51yeE7c78AAWR9zuPH8LEpVRpXMrziVUttTHomx9bPqBxAl4s8rFauYC523RZsaOg/2CxYV9P0E8oW0zon7y+V5vIV53rf+fp6jRSEWForwpUednZ1u+cz8mnMydO/vo457bGwMdVlMFAr8bLH23+HUYTrJ37jRTe35iPSxv/571+Peu+8HACxloBJvvO4aAMC+l+hPXzu8HwBwEWXm+NtPfwoLinLufJws+p4X6ZG/69l9AIBbriOLvXk1feT7e3uwazf97Z8+RK/6hZeozf/AR/4YABAqzvvCzhd5bh+4CQuK3lUjtr8P//HHAQA//D5z2CZU6+TZ55ivs+/IEWyQd/13vv1VAMBV11IPv1ye/Zu2XAwAKFfJ3v9sB6virlg7gB/ey8jE6193MwDgm1//BgBg7WbmBW3ecr6Oyzb9kyf3wutYDwC4/V5W7r33aUYbDh3gfTt4kH8/8ARrHNzw6leho8CbbS5DQyt4c2++nvULFqYZhdi0llGJf/fON+Cdb7sBAPD/fPVfAAA//vFP4XB6sHI9+6NqSTlLRU6mamUfFXm6l+bZfw70sQ9eulyqizn+drk8WeiDh/Zieoa5J8k0n8NqlW0/DKUx7+XY0jfJ9jBXqqA0zbZflwLDVA81y2s0GYbGuEYIaFiDpVRp6EJC28zN8JwHpQQJoyISinL3dWmOl+O41DfASNzwKKNLoxNs85Ci4eDBI/Gcr0P+/SWNnT1SoBSV/5NWPYHi/AIKmqMO5ngdGVXzrYU8j/4B9lELGrO7u3rjcWHnbkYbO8TwT89w/1awydyMDo6P4LmX6fqVzfN6MorynwiO2XdwcHBwcHBwcHA4S3FGmX1jjPNJYx/JTno1YHycK5ikz1MKdWoZ6WFLkTFiYrmjZqW3jFZ1fsMYeP0ttj6skb1r+BESnjHurbripmd9q4e9n0zDk17YVpjmAxvGlXTFlot5bURezLCajj8W73vmzsE/48Wr+amGEUI5g/hiJj3T1tuKV7q4htZqtciDL2FbFPuy6hwtgiDdXU1a/sgL421MsZ1MqNqdWFTbtm5+7JVKvB8Tj9UbToN8umCuL+YUE3v7+j4SKWXpS6zY00M2Yt9Bam37e3pbPi+VSrEOvUMss7HmCwt89sbHydhk02JFwrDFwQY41mHH2v7iirf2TJmbkDHq9uwePsw8nDlV3swXcqhIT9kjBighdn5mhoxTUVrJnHzIC/luHS+JRIL7N+38RRddBADYK022uRTY9XpehLHxEV2XPKMVUbCcB4PVtKjX63F1xlyuEL8HNLWf5s4zPS1nhoVSfF+q1bq2KcHh1GPvCNtuj/TwKgOCu+66H6+/jkz3KjnOrF8xBAD43Mf/DgBw0/l0r/mtd1OnvzTfgTt/QJ/r0aNsO4N9ZKZfdeWrAQD5Apm9Q0fIbgfZNFIdPPbKc+ik84Hf/R0AwD998b8DAC6/4goAwONPU1PfNTgUt6l9+8mKf+zjZOu/8qU/BwDk8jzOffN0z1m3cR327mFkYNVKPl8PP0zG/cbXMidu1y7mK2T1nJvLzYpVa3H/Q9x2y1bmLex4kax89xLuq6g2/bXbqa0fWr0OKzbS9eenqn77mjexiui2Z3icQUXqHnqIbkTJRBorljNCb9HDv/vs3wAAfu3XWP13/x5GQfr6ef0rlvRgdo7Pynt/9R0AgFtuYpVfh1MP86Of15gwP02NeGWhiowmBqtWM0K5aS0jO4NLmTtWLXEuNiUP+nJlHHPq1zKR5k2qflxWzpW56Fhl2Om5OUzOqEq0Ir7zFb5W1FdaXmBkExUPyDFwgH450Rw+yPP2NP/JivFPaSxIBTl4usZ+fTmr53RyhNEIX/k3Ax0cUyLNG2vlGgLT72t/5bLNH9X3awZdrvHcc11pBD7vz/QM99uXUNVf5ZbuPSiHLQYU0Nl9GB0FnlugYHgyxx0X5C7WKe1+SuP+3PwMhsc4jhZHNPa+wmz+jE72vQobR3efQq5qCIUUUFdl7EjJE1YswRL20o1m0hvACbfl4SbjJFFN1CWdSSrho6q74AcePMloLKGianIbWIaFXvxj5QuxpWCbr6bJaRKeJUAm4SUydtXaj7ZV3Mn2bxKg+PBhAzWT/igB11p7M9lSE21JcmohgJANLLDr06Kmrgel+doM5pgExE4hpRBcUcVlrOyzSY9KjTrqWowFsoo6s6vFf1uoK8lozWoOrss0ed69ezd8rR7HRiiFWbKEHXG/CgfNTHGS4qlIXTIRIKvFg01SZ2bYUdrq1TqUarUpzTnWlrN1sdxekAsAzjmHyYS2QLDJ/pEjLGK0bt06AEB3N8+1ES6aSKvTg9p4UtKjQmCWbuo7dA2TE1O4+tqrAQDPyxbx19/3HgDAfffdq/vFiYXJDRLJAINLOZCVNUhZcm3gs89oFsjy4ussKNOqs8M6fi4MorA14dkSqT0vQEa11y0py5KuHU4t1q6gHOTFF9gGTJE4MQY8+fh2AMDyWyhd+fH3OXG+646vAQAO7qJEp6uTk+VvffO7aGhQP3xI1sIhf8d81iwL2aZ3vsjjbXtqW7xg3LiJ7f+2b9OicvkKPr+f+cxfc1d6djeduwW33cbk1737uUh/y9to1/lHH6X054YbbgAAXHk1pTrJdBpjKja1Yg0tQ/sr3N8XvvglAMB//pOPAQA+9nEuGM4/jxKdJYNLcNHF5wFoLnxfdT0n1A89THmPyRY2bqIl5lyxjNtv53X0L18DoClbu0KLlyuv4DlfcC73/dE//jD2dHChcevrWVTrne/gBH5Oi/f161bpnLgAq9YWFllY85m78HwW4HI49XjxBUrNrJioSXkblSaZmUgYoaLCWEdIzqxZswYAMDfHecLUyFGEIfu3uXn2fcUyf+fpWc75Bpfz+Yx8JdgGJYQeJ321SLadnswSNPfTvBaBZDE+QhQlAQ/lY7l+NReaA90ktzpU5K6iInFDA31Yv5x9/YZVfA43btoMADikBPYeyXlKktIcHecs/KWX9+LO798JAChrgrqsX3M8nbuX4viXyhopVkVXJ8fZoSGe07JlbOPDItNMvoMsFxulCjCtBY8uFfu1EOnmY4R8l6RBK0k69DUyiGS5e/gon8fqK+SzOxmPg4ODg4ODg4ODw1mKM0rMBlWuQKIFrooyKa7OCilgQGHXqiQ3BSWaptLG8MmST9EAL2zaLVnIP4wFKVYUgissL6NCDMkICV1yKPatHjPqepU2yJj3RtiUxlhxCSv24ItxT6hKQkoxncBPoyoGPBIrbgXAkrJQ8iwsYVaYRqnUGqhLseRrpRl5eo2Ta+t6lTVVtChCkLKog9jZwEJhOnc/iD+3iISpgxIp3qfqLH+fqmeFuYL471DfT2d5b/Mpx+2fLiwskBXJZklxPLmdbMzo6GjMrmRz/KxizICY5LVrKFUwZmV2egZ5WaaZHMiSeKcmJnU87SNlUakmmsWhjn+u9nl/f38sz3nwwQcBABs2MKHw3HPJFr74ImUI551HJvDZ557FwFKeU/GoJGyxrWVroSrbd7Vitp4RHnyQyYt2Tz7ykQ8DAN761jcDAJ5+hszulKIda9euxcQE2ZuU+ggLTPh6hn3P7Hb1zDUamJ2Z17lwG0tAtgRgi3LYKyIPgba1PsP+dji12LGd1o7r1tDq8Wc/YVLq9VevxKE9jOhkZDhQVVGr++5hAujn/q/PAgC6lGD9H37/f8PjkrscPky51+OP7QQAfOhDvwcA+Nq/UG7jSV56/vlbceGFlMY8+zwTY+/50X0AgIRkq2aHe9W1TBB+5zt+G7/5m2S8d+1mdOGG68ncrRJrb1K+ghL29u7bg6uuvhYAkO/koPmNbzMBedUGsuX/8lUm3XZ2Uerwwk5GtoIgwLt/hYWv7r6LMh2LkHVLPrdyLaNuly5XUaxde/Djh/m8Hp3dAwDY/uwenf8beT8lzx1awcjj33/uc/jzj/0pAKC/j+fwg7vvAgCcI7vQrGQK1Rqf52VL+tGh4lxZJSGaCQF7ModTiSWDvKsmfRyQ7LNeLmFqnDawOSVZ5zr5mRk8FCXNMkvMahQhqflATZHLwOSRUiekNE4dPbgPADBTnMHsAvenqZiVQoXUxMiKLbcCXZ4XYOWQ1AczHKs8TZZGFTWe9Mme9+mcl567GWtWsM1BxidFJQaPHSJ73pXjtp7mVUs7KJ1b++prUVtg9PuF3Yzgrd7I6MD+I5TiRJLo9A3yXk1Oj2GVCsrVZAzTt4TnPFdju87IrCXbaXPaBmaV6Gvj3EpJDY8c5Jg/PE3JzuwuRiOyuTQq4Pjdt8zu17Hj9mI4Zt/BwcHBwcHBwcHhLMUZpZnOWUn2YGxERbW0lPMTwPrN/CyMlOiRF7MmW6a4AJCYt0bUiJXzod4LxcYlpLv1xfhnVBgllWzADy2JV8x7zWyfxMDJXg8xIx8Abfp9K5hlut5Yq2+3M/Cbyby2H9WeDsQGBunWYlR1YyqjGgIx7Ah0g8TwBw2LapheWtovP4lIFcbqIGtlyb2hmP1QOjhj+IEQDd0LW/IZk19XlKCkw1fi7waoazlv1p45qxjhcMqxdBl1+MaaZ9Js1+ds3BAng9pzYfas1QpZi0PGzkg725HPxgWqTDdrMPbQ2rex3FEUxdEkS1S116amvTVhd//+/bH+3vTIH/3oRwEA4+Nk1letWgMAOHyYeuitW8/HgUPULHsJK0duz3kzQRYgkw8AGbGAuVwutvQcHh1uuZ4vfpnFfvKyiFs/sD6+Z2ab6et6KmU+Y0mrZY7WxON6PYwTmY3ZN21+ox62bbs4wYy/R1wMzCW0nxZcvJlJsVPSs0cquDh+5Cgy6q8P7SVL9u63sTjUvhdpXXfNZUy6rZf5u33mU5/DpIpo9fVQE7xqFXW3n/40owA5MdOvuoYs+74DB7EgC9iHH3kUAHDLa24EACxVrs2zO2jj+dMHmfx78WXrcc651A9/8EMfBACEekafeeYpXodCzl/Ts/Sxj/0XfP4L/wgA2CyNfGe3We+RQbziSuawTOo5/9a3vsXrPzKM3//gHwEAenvY/s8/j/fthptuAgDc+b3vAQBeeIms+iVXbMC11zHhff8RMqFTc3zeHn3oYQDA2iGykHVpkdeuXoM/+zPmDfzB7zNJuago5TNP/QwA8KlPM59gi/TTY6PDyGQVvVMRx0HZ3zqcenSqcObUBMeJI8r9qpdLmNbYYQWjehX1KYhhPzTC58jykOqoYEERmpEJMutmIDEyzjYTZjiv8zJV7buKjIQFfmu6JMwPJa9iWHnZIk+OTaNsOQGypO1UxOt3PvABnouSh4eWsE1GlRqmjnC+WRZ7bvmhO59hxKqmzNlAfX//Emr8ewoZvOEGPt/rVvF+LV3NMfnlYbbNSkMFRyuMACwbWoVcgc/jgYO89mJF+VolbhMocmXRrc5MFr0DvI60bEHHjvKcq8oNSDPYgD4VQ+vp6UKjYeoUzQ/bimO2wzH7Dg4ODg4ODg4ODmcpziizf+tNlwMAnnqSzMfcjFaQVWCgn+x7ZY7vlZWWXC9K82WFAyJZWqZChCWyHiJxkJP7jom+IrGeSa3+Ar8RWzR5dcueVpEtz5aZtlqVxj6VQtOkslWr74dWwIqfmra/Ua+jkbT9SessptI01Qmxj43I2FSuVGu1BiKdi2c2nb4xh9w28mwprGtoNIXUjZocQnTKkcyN0j4AACAASURBVBVFMkbf9gUPYewhytf5hr4r1tPyI0q1ZgimoiJBkRWqkNbP4dSjo0NaP+nUzz+fTN7g4CC+Ls2wYWCAbMS8WLFNmzYCAK65hkVsstksHnuMjhtPP7sDQNPS0xiaQJSKOdM0Go2m+5VeLZLQ1NQrIuaZvjIXa///9E9ZICif53WUy632lvOyZxs5OoHuLp5/IEbIdO/mcJJRSfVkwYpc8Rj79x+Mj71MkZADKsneJ3s2Ee6xE878/CwyyuPplLXh/KzZd6ZarteQTCbjczLrTct5aN4TFdcz217fjyMjcUQi01oq3eHUoDpHFvu8dcwPeX71kwCAiSMV3P7VLwAAUho7nnuCev4U2AYee5gs+sghPmdXXroFXQVpfgfZppYPUYdrdq47nqWGN5fnuNXTO4CDcu7ZT0IP/YNk9CemmKv20i5+d+Ua6n4vufAi/LdPfRoAkBVr+su/xKjD5i3Mb7nzzu8DAN765lsBAD+5/z5sOY+Fryoay65+9XUAgF0vMzr2DbnnWJG3NWt5T44OH8K6DbyOQGPLAw+zL4CsopcP8dze8o538ng/fRh79u3jea9jFGDvg+xHBvV8PfYox/Nr5c5TLS4gpZy7Sy6i7elzO5hTcff3GGWYnWHkZESF9ZYs7cPoKHXXvbIR3r+fxx1gGoHDKYSxyqNjHAPMlSyV9NAzyMhsh1wTg5TNYdiXlUM+J5UKf+NsVxJlc0AssE0mUnzt9Tlva/gcl5bJoaZcT6NSs5xDm9/w3Kw/T6f43VxGOv3ZKUiogFtuZPSqNMfzXy3Hm7/6FC1eL9hMJ6frrnk1RoYZbUiZU2GF+VoTit6ZusPG24T67GppBqvW8hleoShTKLfIDuWSlhUVeOIxPgMbzl2LZ55j5GNIEa+8/ECH5BiWEXs/MTUTX38g5UdG895LL2M0ba6oAmeKXjeUp1mvVOJ8MsvpK7iiWg4ODg4ODg4ODg7/NnFGmf31K8ii5QKyFtMTXAEVJw+hWucKZmqaK8ykpOA90tumpEdMpKSFL0VxIZ6qeWGLPQ/ENtaNebdCXV7TId/XNr40wkkx+uZqE1kEIEjGul6jy31z1tBKNKi3MvzU1Ku4lb6akMtPKq19aDUWiPGry347ihqoa/UYRyHM3UMMfIRW3a/neU2z/MByD/hd06FZdnxgWv6ojoZ5pksX2qjys6SYXa8ir/6a1TxIoy42tiJnkqGVrbkHDqcO5ks/KR1yucx739XVhazKc+cyinil2Z7Gx8gA7NtLx4yamPF6LcSRo2Qerb5Cfz/ZECvVPTtLdtSclsJGhHpomn218dDaV6trVWCOTaUSli4je/jSS7sBAMuX08f4wAE+7696Fd1I/IDnE0YeGqEVMmnNG2g0Ui1/WzQqlSJruXRZNo4cHFRBsRVDa3gvpB9dJmeGsTHqIEulIlatokPD7CSvuVTmfcvn/JZ7Ymy+7/uxC5ax8/acthcYS6hPCdLBoiJd/CyjnB2HU4t+FZB69H4607xGbPdvvec3sXYFczU+/9nPAwA61HZGD/G5yiU42KQT7NNmpou47lWMiFXl9tGhIloWzXnP+94LAEiAz2Gx8hzGX6DrzX/6Q3729//AnJG+QY5hy5bxOXh2B7XChUIBr33D63kuejYtQvcO+dKvXk1XnvFRtt3h4RFMyFVq6Qq670imHDOFKUWtRuQ4tWqI++jp7YenPK+yChi97g28T1b7YkK1N2rKQ0kk07hW98JLcr/XXU99/7Yn6Fhkjj4PPEBXrHM3rUeoCPDrX/taAMD6dewTzPkrrXEpI3e64tx8rDm2Z26gj3UPHE49RifpUAVfDmw5/t6FXBYd6v/9NH/D4QlGpBIMUCGT5W9mxQqHhpajewnHn0wPI2FZ6datnkMl5L5KKj5VjbLNflNTl3pNeVJ1mzvJ9SzBdrflza/H/pfYx/d0KmdLOZZHVagxqYHivnvuAQBcsHFLXNDL8nD2vczrObSPr3vUr69QZHigR/PUbBJPP86I1KFJ7n/zxYxujWgsmalx/FBgEUeHRzE3ywdybprXWsjwHmT1/PTlGSVYmGR/UyrX4GteWJ3nePH8KB29Mhrn7X6mlLeXSaYQNjSW6L419HoiOGbfwcHBwcHBwcHB4SzFGWX2h/exTPjSZaos1kU91GShhhE5JYhchExEMDTA1X1CHslpMYulTANFj6ugsljteqKV2a9B+ibTtocRPHMPEbuZ0MrQl1Y4siq8ShGvRWGsJfNNi2v70+0LrOouYtEZfPO5F2seS/jN7SPR+p2EafvRQFhXJTnz9zeGX8xhFB/P/LwbMfVp1y6pPrykuajwb6sUHDZ8NFTW2SIgDdkjmW94YoHbBmJp0olknDkfFfndjOc0yKcLpmnfuJFsgun2nnlmB9ap7Pj8HNk8q2ppTjjmNjM2pip9xQoCvZfW77uwUNJ++ZoQyxZXbI4ihK3pIYiC1qiSsS/G0nR0dGF8nOdirjuWR7B+PfMIXnqJnuLmUx8kUiiXrOR3suU6jO0zlt4q695yC9nFt//C2zA8PNxyf8wJZHBAOuuX9wMA+vr69P5y7FN/UxCLFTseibWy2hnG8HueFxfjyBfItszMkjm1vAJ7tZyHVCoVM6jGViZTzfwah1MIuSkd2Ufm711v/EUAwH//v7+AW298EwBgwxq2vxe2k1kfHSbTNz/LcWTTBkacVyxbjnvuYfXlm1/DdmbRoy1bWY22Ry49E+McsLwggd/6X34XAPDk08wX+K9/yZyVH/7oBwCARx+le8369Yw0fOB3fg9//TfU7E+NMlftf/0P9Ke/5gqe6/PP0zHophsYDdu5ayd+9/f/EAAwJib/G9+kZ/61198CAHhOLkMZ5RPMK7/l4N69OE+5PFnpiK++mvtdJj1xWSz9N+/gPi+46ELc9m3+/9U3kqU3Rvfii6grnlQfk1Z08cf3/Ciu/n3RBbyny5ZyHLco5TkbGG0wx7y5+Wkk9axbNKxY5Hk7nHoUS+yjC2LI7d6H9TrK8oefV45TpD4wJVXFps1sQ6OT8nzvTMCK9VQ0d5lVhLYe8TmZKfI5WbWOEdUawkURUX7XfnerfhvGDmz8e3zsKM7dxPyTgU5GpbPgGFKb47Yf/N3fBwD8pw/RderAy3vjSNehYfb5e17m81GQWqS+wO9OTzKqNa8cue6uPF56kRHyx/VMX3U9n5c9hzmm9Og8brqejl4z5RlccTm1+hXlgaWltrA6BeBwiOp4M3rsqw5BWfcvmea4NDPOjV9WrRCVqEImBfSqUm8u1cyxOxkcs+/g4ODg4ODg4OBwluKMMvsrl9GvNZ9V5bCiNOHlIkTYY5ky7/uVXZ3sJsO3pJN6x+oUWcjdOw/ELjkL8p+vi52vSss/2MfvRvK6TmdzKIstjVk4aaHMscd85H3tMx0kUFGZ3VDa9XrMxhvFLt99XWfgBWazDz8jTXPCRMjyxm/YrZeDhzT2Ca+GhK8VrRj1sKpaA5ZxoOSAUNUbPTQjA1bVzlbNdtiEVbpVWKBar8fuQb45hciJpF7VSt6Y4AavoVxroCudbfnO4V0vw+H0IJHkyn1ikgze1BSZ5HM2nhtHk8zNwqriWjXLgpw4jL3v6+vDpNh/09/39ZNt2yc2NJ2TflYMRCIRYEFs+dKlfP7MGciYh1DtaW6ODEQqlUJPL9kce8Z8MRsNRZCyOTKOl19Od65f+Xe/ju1PvaTP+Mzu2rVL18U+Y+NGsqGmv+yWrtJPp/DNb/wDAODmW+hr/va3/TIA4DvfoStJbw9ZS6teW1xooENuK1HDNPV8UMzRZ/fu3S33qrOzCzt2UOMZ6ObXxF6mpaPs0TmZy1E2m43rBZie314dTi3SqpvylX/6EgDgDa95DwDgsQe/h3/8G2rnK/LoXjlArfvOHWxz//mP/gQA8Nm/oZPH2NgENqw/B0BTh3755ZcAALp6+Rtv2/YEAGByku3ngQcfxtPP0uVnYAmfq69/45sAgAsuonvOdrGDCyWOQXfeeWfcVt75TrrffPiP/goAsHkz/ectgvWIPO1934vrVzRUidPcoW7/zp0AgIIq5/b28DzyYvhvffObsHcXKwEHCv1ue5Ln9KGbPwQAOKA8oSe20Q//+d27UVTtjqeeomvR2Dj7oUFp6tespIPPCuXmbN26BY88/JDOrdUhxKpnI5SLyho+m7VaDUX1NUGqWeXd4fRg+fLWusRJ31QLEaoWWVF/rfIRsTvf0VFGZzZvYZSr0JFHuar6SHXlXGbYNi3C3K2oT1lzi2Q2E0eUu3vY51t9lNESo7gW+a1Ix97X04MuRSLmJjmW7drH9lSZ4X7P+0VGkm68lnkm9WoFDc3XrGaTnevkJJ/DpILVy5bIvVFujtuf2gFNA/GB32a9iF0vkGHvLLCt1zVvy8ktLldYgpzPiEHa05xS579Cc9hI88kVWfZDyVwK2W5+5+g0o2Q7D3GsWagqr7KqPAo9N7X5CJML3K8+Qj57cjeeMzrZH9QP5aM1fJP0fGjugJwuOi/5TqGbCUvJgN+dH2WHEDZ8JM0yUB2KrxB8KmHSFUkDFKJKp1PwQpMocD8m57FERLPrTOmEyvUGfEl9PFWZSJivpSa8jVqr3AYIUVXDChSm9HROvk26bbKvhyyQRCCBEJ6+G78qgcSPayaYfabe95qJkjaZsGu20E1YtQJA1fhV7RRp3ceyCnvZBMf2kdLkJhWGSGlyF2gBggUzPnU41SjXLFmVv2m3kp9mZosYHOCEep0mJXv3MNzYr0n/yAg7jYEl/A58H53d/Gx0jIN1pHZtE3ZoMjw/zcXFqlWr4EumVVXb6OvlItwsCE1uk9bzwokHW93MdGsYvled+rnnskN+7WteBwBo1D1ceSMTFcsTXEysX8fJTl7XOa8Exaef2Q4AuOaaqwAATz22Dbfe+hYAwN/93d8CALZv5zbvfS8TJU0+YVKndDqN2QoXJ4U8r71LE6Tt21n4yCby/VoQjYyMxBMwez5sQT2lhEn7nUy2NDERxvcntvL03ATmdCCf5vgwNkw5zN3f+TIA4OiBEQwpYXz/SxyorU/cumULAOATf/ZnAIA3v/nNALioXiFZy4ZNlB3ccQctI2uyvtu8hTa46SQXo+edd15s9vCWt1A29PXbvgGAk3oAuPzyKwEABw/tA8DJTU8Xv79SE+aPfoSLlG9/kwuFV11Di0GTToyPj+PBRylH+KV3U7Zz708fAdBsl+NKRKwoYW/pAJ/Z7du3Y1CL5+5Ojpk3azFhk/Jnn30GAPCGN/DZ/KvPfQubt1JuO7dAKYYt4scm2MdYLZ+t5/G5npuZwM033wwAePhByqHO3cJ+qrOD4+HaNZz4TCiJOIwqqGs8y0ROcHC6kU+zHZh0plwSMQPA8zjJzuas+JNJhFVsUwUdp2bNVriOMLYflgRYlps2gTd58dgcpTLJeg2lkpFKfDWziSWyrO3t5MLait2ND0/EsutnX2A7vfS8SwEA1Rmey998hovlsSPsB6686moMDzO59pfeRWnfow9x0XrzTZdxX0+yzx9axcn303oGRkdHUZMtaF6yPUjK5JnsxhMxKtvOSlTDxgvZN7zmtVxwJLVIyqfM+ILfMcONRDaJHbu5aPna3SSoUJJhTMPmlkpot/ubDZDReN0hi9J85uTmD+6pcnBwcHBwcHBwcDhLcUaZ/VCJF4HkLwWt0pb3LUXfUrKOXX1ccQ4MkJHssTrB4wq9TJBdqFRqyKgQiSdGJYzMIlIFuZTgYUVvUqkUAl1y3az1JGUpi60IYmsjFb2q1ZGIiw6JLVcRq6jCfRij12hqaACx8V5DtIcs3CBpDnTO8CwxRkm5CAHJcyAWCZDFX2iFwCxBWPE1349Z/6pvtlXNJEteB/dVVUjWCxtxiC2hpMiKkty8uDiQ2XYq6uAHzfeqds0lOJwe9PXLrk7yHc9nmzw6MoLxUYXS+/ncZFR4REQA8h1klH01jMmpGcxL4tMrps+Y/JVKmpqaIuvS0d2tv6fjolmHZW22YgUZyHPPJXtx6CDD/v/0+X8GAIyPjscM4xNP/Kz1gsTYGdvT2UE2feeLezB8H9kWY/RNZnBUrMu55zJJedkg78n3v8ekx8suuwTPPEMmprPA8/7kX/w3AMBv/dZvAmhKJCKxI/Pz83HybqVMBqikvmlwYKnu17jun8lwgEOHeK3Ggtq9GVIhopERRh8uuOBC7mNyEgcOHND5k/UcGxuFw6lHV4HM25J+MvJRhQ/C33/uH7Bq2RoAwIZ1lIJ9++vfBgBcdTmjQwMDbIdmorB0cCD+/dOySDYLzINHGB0wOUrCmLZUDlsvoFznk5/8JADg6muZzBckeC5PPsnn4Tfe/z4AtNNcJTbxjjvuAABMTZItf/vb385zvIJSt5d20txiYWEBW7fyfJ9+ms/Gxo1k/WcU1j98lG3MEoHrFdltloLYXndayZVHj7JNz8/zfYtePbWDxbZWrsxgcJD9xf4DTITPqcClJTS+2q5T42OxWMTS5Rq/VYxo2zZaGG7azL6mWxHIQExprVKOrbRrNVlq113U+HShp4N91hwYSV1oNKOw1q+ZbXhsuyylxJJ+tvlQCoNarYJi2Yo/ka2u1ZWFqm18RQWKej/fmUVShaT8SEVPuyzqyW3HRtgmZ9TO8kEGu5R8fvgAxyO/zDbXl2U7e+JRft5T4D5WrhjCpdew2Nue3ZTtffCDjJ699CIlbTZOzM4wov2UorvDI9N4tRJy3/kOSkOtOOoDihafs5XRwfMvZfG4VevWoajIeE5zJARmnc6X6gI/T8jkJJcoYGXPGgDAJZsY/XtuJ8eNBUl+5mWFi3huBsxbJFnzxcC3e358OGbfwcHBwcHBwcHB4SzFGWX2a9Nc9aUy1O31ZMk+9nR2oJoXgyZruvkSmfF5ldAebHD1NzvJFaRX95BU4p8lrlZrZDa6pI3yteKJGtImh029rLEQ9o6VbDbGPzJtuxchKRu+lDRRps0PrcyzyXG1Lz+M0PBtdWxaen4WVpvaOKBZ8KtZxMfYfCCKrTXD+B0AiKy4l1bakd+IGVwrFhaZvl/nWldisNl3Br4fW4hasSCTbluRrbrKRtu9QegjoY0UQECj7hIOTxf2HyLLt3IFNYwVJbD1DaxAUr9RSbq/rBKESrJLM234vBJ0B5YuQ0bJ6ZGeG2tVh4fJSBtjXS+zrZyzYUPMYN72DWoJP/WpTwEAdu1ijsBVV5H5KIlV7O9bgq9//esAgF/6DTIm3/oSkyZNszw+Ttb0Jz/5KQDg7z73T/ADJhGuWU028i1voX56aCWvfedOsjBf+OI/AgCef57MY6NRQ6jclhkxM5ddRi3mbbdR9/yVr3xF58yk277eAZTmZTeqqNbwCJlOz+PfiBjNa9T5IPV0L4m1+NaPBL4ZAuhGKkl09659AIANGzagUubz8czTz/PYsv90OLV4zS23AgDu/dGPAQCjw2S3X3jhJVRm+APddC316f/xP/4HAPh/2XvvODvu8mr8zMzt24tWXVrtSrIsuUhyk2zcK9jGxlRTDaRAQhJaAiEvhEAIAUIgBDAQME7AlNfkpRob415kW7JlSVaXtkgrrbRN2+7efmfeP855Zr1K4P38fp+1/nDm+efu3Xvv3Jm533qe85yDRx4in/ztb3sLAODf1U7nzp2DeglDpDMywJFtvaHo+9SWLtpIXu5vHngQtbXsP5dfcSkAmmYBQG0d56P2ZURTK2owW5/bgpWdVKS4/DJ+ZlQ8+H172O96DhKpXKIxYP7cBejqI2d/cISI5/ERtvuKJCJOU+HkE088AQBYMI/IfGfHUuzZznqWDeez4NikeWuE1ht6e+ONrDs4d8NGPLGZGYkdu3lO8Rjf26K6uoPdHAsWq86hsaUZ9z9AU6Plne0AgMN9fE9oUmfZeM1xyWQcjsyTTEJ3zJyKopj18MU4sMxUjaRYPS8eIvs2zp0s6Tg+xrG+qnUD4CCRYD9prG/VcVTnJyQ8KY57viLkPyhgUrLRQwP8vafGOe/UqFYgIdZDWbLMicZMWNx6xeVXAwD27+C8kJZwyMUXse1PiAGyY+vziB9kViymwu8RzT+nr2S2OK7/X3gh57I777wLALB69fIww1YvU73vfPcOAMD1N93M61XmakkLM3Sjh4dRE5fRqEgcEIJv9IuEz/FgStd/ePAwRsu8L00ZZryKOV5zvqx53GRIxdTwAiBQ5bSfn2nC+rsiQvajiCKKKKKIIooooojiZRqnFNmvSh7JL/NrE0IYaxvrgGaiIJWkpPzEL65INWeiiwiEn+UOpyFeg4yUbmJSuvGFmMQlwZnUXsbVDrVUKCLQ7icIzORGcpPavZb0/6LUOGLJJFI1yfBvAAiKJokp7r42uCZl6fsBHKF+VaEttksOxONyjGclzqKpArkxL+RlGdIewAy3DNk3/pYeHMdARcRcIZI6l4q0o6yewRCVeNwLzymvivyKKRMpc1BQliGn660EibA2wO45/JkmS1HMXkxNWVvkvd67j0YeixbMRa2kzeobiCz09RBpXLpUcl5CKyxjtP9AFxaIbz8iVZrWNvLTW9rIrz37zLMAAN37iSbuO9CFq66hSs7d/4c859e/6VYAwM9+So7xE5ueAQBsvOgSAMB//udPcdaZNNvpP0DFniEhj+OTRC9+++AjAIDNmzcDANo7luPP3kujoMOHiVp+8Quf5TnsJyrT1EQ+/pZnqTyydi3PtbW1BVllFUaGiNa/9c2USVuymNmCN7zhDQCAq68gsrty5WmhXOZjjxGBPPcGZh1Myaeq/jsyRES3v/8AOlTb0NBA9KVQ5PUUlIVcspiorxkH7d51IETyv3sHswvveAf5olHMbrzj7X8AAPj+f1CFx7i9Q/0ncM4ZbI/GQT4qnvp73/seAMDHPvJXAIBlHZQjbG1tDZHpSy4hcr97N9vye/7kMgDAQ4+w7Vr29bzzzsHEOJH28zeQZ29Z2sN97LdbtrC2ZOlifs+tt96KbZK4NKWoOXOoPvfAffcDmEblLXO38YILkJAp3rjqWVaqfmbvQZ7jgX3kJpsKzxlSDkJQCeUMu3uItN9wPY2yhgaYCTmicz39bNadxFIpLFS9jsn79h+TUV/BZHn5PYbEH+3rxR++8zYAwJ/+yR8DAK5/FZWD0jXsd4ePsp/XN/B8Wptr0dTIvpLI8T3ZYmSq9VLF6MjYjOcxj/c87rkhLcDBTClKQ/xbW5jBMdnYSqUU9oNKZWZtYCGnTE5WqjYNHJMrBReO0OpSQWuVnJ7rvVVJZJqp17HuAXQuZO3M0Qn24UKWa5MTAfueSWC+6g3XAwB+8pOfYF075wpTUzwuqekxScjGtQz+4TOU0/3tfTSR27NnH3bu5vzzja/fDoA1MwDQKvW2v/zEx3kDlTVvqqkHpI4DzUuYVCZklPc80LhgyoYoVTAkCe2Dx3lujrIaMPaKY/WpMoMNPCBmNanKvPw/WBYRsh9FFFFEEUUUUUQRRRQv0zilyH7SJ1oWyJ44VyZqVi6X4Ve0C2qQMYB2NKk4Ef+BfvIFjaeUdhOAtN7jUuXxZQZknGTXJO2FvBeLPvyTdj/xuDjoQrwDaaKaik19XS1itURSILQP2mlWhIz7vtUGVPVyBY5urWdWW0LNfVPScazeIITn+f54GoHD+xJod1d1jEwvdN4zpF+Hdpzp4yZMn1V8eykaVLXzNk6m73goi3OX1y68pHMte/p9pDaU030OYt6LMgO8F2auFcXsx99+8h8BAJ/69CcBAG+6lajwj3/0fdx8AxH3e+8hwt7RTnWZoSHyETdupNLI9p00+mlv70DfMappJGQTPizOrz2vbyRa4Sk7dMMNr8YCaZTv3k3O+cgwUeuuLqKIY2NE875x+7cBEJk0dP74canTnEmlAtP/futbbuM5bqDF+Oc//3l881tETv5T+uJr1xJZvOkmcod3S1f5j/+YCO554hw7ToDFi3mOZoR1003kWZr50Nx5REv/+Ytf1us3Yf58olMnRsjDf+7ZXQCAqrisSxZTF3xwkIjnhgsuCfXFR8SrvnAjedYDg7yvD4kDvn49keQDBw5g44bLAAAf+uDHAACBz3sbxezGTa+m4lIyRa59Zwf5uBs2jGCwj8i0qUAdPkwjwNu/wb5RkbHa4iXMzGzfvh1rpZX94IOsATj3XD43VZk1pzPLMzzM9jF/0UI88Fv+/rk8+4SpleyTks5117HPPruF2bC1Z56BuNDSfUIQfyJlqRXL2gEAba1suwP9bGNLOjuxcSO193cLye/t7QUwrZqyWJmDVIYI4z33PAwAeNc7b0GlyJqYQ13MfCxexPda3Yuh932HeMy5CxZj9SoqSd37AA3GzKSoWR4BvX187/p17LOnrT4dX/nqVwEAnuaLAalQzfeI3lv2q30ZFYycWBUlocJ23nPNIySKWY9KiQi1Td9ll+uEUrkQqqYFak+G7CfFi7c6D2tvDLZ1x4r5MHNd4FRUHyhUuwoPrS3tPL4NiY4YEzLmGh9lm4lL5/+1t96I7VvY/1aotus3vb8GADz6COtTzlxJdZxj/Zx7zly9BsP9PM7y5fy+Ky+l+eJxoelmTPn2t70NAPDjH/0IALB69Wrcfy89Mizz8dY3MUu8fy+zZ7/6PjOJN9x0CwCgPDwMv8JrT0rR0dZtxSmi/0OqQ7Ws+0Qhi54ujklbdvP6imVbk2lNad5K9tsEgKMVYFzn5r345/hvIkL2o4giiiiiiCKKKKKI4mUapxTZb0kTCQj5XKNEQAZHhnG8l//LClUua9dYIzWM+mFZ0FekjBN3UZHySKqZ2YCq+PC+FEqq0jf2BfFXq0UEFVOwMe1Y8eO1LbL9qKl0xFOJaQFm4/5L2abkS/9ezn+BkPJSuQJXSH4gZCPmyUnOtGVD1Rzj9POzFR+AdmqQO1tgOvvaNQcn24g7CL/PCTMV5af7twAAIABJREFUcv0tyXNAiH5V6KQPP/QhKJleqzhyFe3ks8pyFFTHkHBS8FQPEVOVvWVCopj9uOsHdODcupXKMw1SBLj6mleFmaJLL6VTZV8vkbpLpVJgfOEPfujDAIBf3vMrvPZNbwYAjGWJ6nzyU58GALz6FqISP/05uYrXXEZ+7a1vejNq606bcU6izuOWW4i4n3cuawQ2brwIALBixUq887Z3AwDKsvru7u4FMG1/3iek1fjx55xzDv7lX/4ZANCxjEhjZyeR02RKLo5ptutDh8k1PnqUeudDQyOIXUwVhWuv4b3Y+QKP+wfvZibka19j1uD6V5Gf/O533YZG1TrcfjvVfdqXUhVlzx7pmU+yXd/06tcBoFLDrbeyXsE4yj/8AV1VP/7xvwEAvPY1vL8f+Sg54AsXduCxR58GAFxyMe/pTumXRzG7sXY9efK7d1Gdo1aKb489/iRuewt/F9P6Xr6Cv3VrEzNZmSTHyIkJZrpe8YqLQkWp+fNVzyIOu43bz4jfe/rp0qXftg3nnU83z127+Bvv3892ftll/H9ZtU+WLTpy5Aga5LC8YvlyAMCwuPPmy5CdYJa3UuHjgX374QphvUmOv3f/jOij1cBlpfJh3gBveP0rAQA//+lPsXg+2+6yZXztrruETMr1t1tO3KefzexUoZBD7yGijoZEJpQ97u8nwrv6dN7P7m6+z3MDPPMMsyb1TMzj+W3MWFTOZPZkTg8zCFdezexYvjCOXJ7XmM4ISU4LGY1i1sNxOAf4xkbQWA0vBgdiYIhTXhCjIV+Qm3rFFJT4O7lwQo8EqwNMJMVssDpAeRNVKvzMxHgOJSnO+IE+G6jmMq56SvXXhUtYa7bpyc1YPJcZ2d07OE6vkIP8ob3kuu/YwQz09VeRs1/K5TFfru+VHPvf2jPYl+9Rv3nbO94KANj6LPv0BedRze2JJx/DCmUDzF/osouo2V+aIP/eUd3KjscfAcD6mLIYEV0D7A/mol6VAtzoCPu4ZUh6B45g8176zLygeTxoY8fJio+f1fqtovWqF7hwJfKTktpk0v39y/kI2Y8iiiiiiCKKKKKIIoqXaZxSZN9MZB05Wca1Y0z5MWSkJ1rKaZdXNb17qdQUhPSL4JV0Y/BL3O3UJsk5Lkmlxgn176UXr12nE7ioVoyzLuRdt8CPGTJurrLivsOBY4oz2gVXdG5lIeKucaU8O7YP1zEtbj4mpdXvSiXFPuSbWo7UfyqFKXhQTYAb3jA+mq6ttmjTKj0unXfxIre7UDZHvH/f3O5K0+duOvu6F5Jrnc4yaAevy4TjePCkQ+55ureViLP/UkW9YLGP/vVfAgDGR8jHb2lpwuteQyTuX75Mt85+KYxcfjlVbR59jDzdL33pSwCAx5/cgvFJohAjctH81Cf/DgDw5re8DwAgUSYM9BI1P+O007Bz+70AgE1PE6G2zNjQCSoZnLacyP/cueQW7921C49v4Xu/981vAQA+9slPAgBGxaNsWkB0ZpNUeVpamnD1VVTK+cpXyKu//etf4/cIBVmxnOjhgQNEPoyfuGPH83jy8ScBAHUNRFA62vne5SuJIl51BRH/Sy/jdxzq7cbPt/8SALB2LRFMq1+4RVmOzhVEWm97O7MDR44exbveRefTTU89NeOzxns1HvLwCB8XLliEQ6pfOCbeqOm3RzG7seEcIvtf//rXAQA3XU/d/S9/9es4oAxSYYrt3lRF7r+fijclzQ8LF7L240h/H659FdHwYpFjoSft7AkpSi3tZNt68GHx2IMKBtSGbn0rkcLPfvZzAIAWqV5t3roVAFDWXNc0Zx5yyrLd9SPWqjSrfRzoZh+01/uPELm84YYb8Nw2HqeosbxRHPe4VHqa5AK9X14UFc2Hi5cuxRmriYTufIFI4nbV4qxZx7qa8y9khq6qOaHvWL9ZtaAmw8zigoX8vqMBlXvGpcJTEvI/PDSEztOYmdu9k3UFkuTHseNEM598mvftltddCwCoq8+gpobn7yhDMSFH7yhmP8xnpGC+QlpUpNMxxITK2/+szNF45PkSP2PqNm4wrVJjfj6ZCttIQuuqopQLM2qr1UoJMUf1maoRK6nm0pBxuOx7sRKPMXhkGPlBtrXXXH8TAGDzJo7F5pt08y2vBgDs2s92vXrV6Xjst1TWMqW6PbuZ/TPH+J6uXgDAxotY59Z7UEpV114f+gRkx6Xb/zz7zVlnsoZnxzYqYiXk+B6Pe3hhD7+7oDXY0Ajnyl5lzUYGOQ+WteabLE1hQuqMmRbO+SNV1Z9q8RXXWi0hxkYsAJKSF6vVOdYkf38m7NRKb6oTl0ozbbDjrosW/b4xNcL8FFN6kE2wyVyWMxwMM3UNaKlnKr6QVfpHVJmC0WmqHJidEhtprZtEQcWm+ZKlRSwVJempOBtjMsMUr5PIAKK7lHUco0hUtVAv2CpJg3gsBiRER0o4PAfPzKwqZtqjog2Tr8rzulEsoqr/mQFXKNOpX8sadmhF7fsItFB3JPtk1JyqViKOUq++OlK2WIDtGszYy4wpxvNFnbMkUiXFlc8WkVR6vFYDf+DnEMVLE5/+NIs6P/DBPwMwPeH7wXJ88Z8/AwDYt5fp8rb55NccPc4J2NFAedVVl+gzZfzqFyzm3bCBBX6bHnmE3/OJP+dnZdazYB5n5u/e8Q2kEvy9K2pPl1/KBfOnPsnF+OtuoTThfplsNTY04LpXkrLy5ltZzPTLn/wAAHDjrVwEDaiQ9sEHWFx18ODBcDH/0Y98BACQ1yJnmf4fU//8t2/cCQDIZTn4JpJ1WL6MVAqbfD4jetJt7+bivEMUnd/cdx8A4MMf+CDGxzi+GI3gR3eTMnX/g/cAAP7makp/fvPbLDScv3ghdu/gvW5opXzchvOY0h3UJqwooOK2d78TAPDYw4+gpY1jVGc7C8rikkyNYnbjha2UwnzFuaTMVDWGPfvwozh9BSkxFVG32ppIzdknec6rZIK17QVSTaqOi01P8Xg33vRaAMDBLva9pzZzcn/ueQpGXHo5JVtf98bX4U/+hJvy/jEWqxclsPD8Prb3I8fYTtas4ULhQP8w1pzOgsL5y7nwSIrm+fDTXNBfdgnPrXyMr+/tOoyLz2e7GxriomHOxdzo1Em6sqzvXS4p0R/eTbpZserDF1B0eJDnYtQ6P8lFxpTv6P8pfSbAnr08f9fje/bv5eZp0SJScZoauWjrE+Bw7MggMgnOD+kMP1PQXDZmi8wqaRCpWglXOAWUDVQqSVY4TVAgitkPvyTREVvsa3NX8pIIJB4i5g3KJvYhUCMuectiyUDIADEVo8YE4Boo5GvtkBH9d3KcxbANdTVoEDWzVLS1kkmLS35dC/u6Dn52NOfjxutEz5lkO+rex3lnkQrNHxKdZuFCPp+s5FHTxnY6pXXhwBj70oRkOzu1ce8/xCL4/bs4LmS8VAgAHD3Gtn3hhZw777jzu/ysAKWD/aTsjVYq+N8P/gYA0DMhuXiN+Uk9jpf4/Q3NXGMODOUQS/GeZ2p47THR1xeJumtmYlVRAWOVADXaYHz5c5/Va1qH/o6IaDxRRBFFFFFEEUUUUUTxMg3HJCZPRTz2rlsCYNoswZH8k1ueQjXLVEd2jIhDKT8TMTa6SFrIfm1NPdJp7jDD1IZ2p5aucB2h5ZUhPa/Ad4gQOgl+1tEutaLdbG0Ld1uLlnBH5yUB35csVZEyfcUpPhZEiygr/eRqKxwLPMSUOk3q3KyoyVLIFlb0ZKnkSqWCXI7XblJ/VRXBOtrJuZJcsmIYxwF8fU+QIupYkkRTWcco5niuRWUhSqUXIfuS3Cxohz1VNElRnmtBj6WKA08IUOBppy4zifd3j/w/hJ+i+P8ar7354gAAerqIrM1pJXp/9lmrkRRAvO15mvL80+cp01nW7r6oIvV//dd/BQAc7juKuSpueuV1KsZT4eyZZ1HGctcuItdnnkGk/NrrrsGunUxJ/uxnPwMAfOpTfw8AGFFq0iQ4n3yC6dQ9e/aGVIqkUL37fkUqUFFZpa9Klu8976H51TXXXINAsNFjjzG9f7iH52bSob76wKgKo+prZMIX+PjIh4moNjQTBTnaRwrEIT02N/K+zV1ARPdQdw+Wn0Y6w3iWCH9CSObgAMefVJr9taGe48HadWfhwQdIjTIzoXyO/X7JUqJIR48QGdqzlzKeQ4MjoSznhRuZAfn5L2hO9sDT3VF/mcX42iffHQDA888Tef/wB1iYPm/OPDQ2MFN1XEXdvd1EA5tb2F6mcixofeSxRwAA9z/wIK6/gXSA39z/KACgrLHddTlfHJeU5Gmnsx1193aFcq459cEeSWKeccYZAICBYc5DZqC1oqMTWzYTwT//HFLCdu9k21nZwUxQ+xIW0vaIWlCfchEr8nzXn82swAlRDOplyNWxgtS6x55mwWFaEplTpSoeeITXY6ITx46xLa87h7S1972PfdLmmkcffRj79jOrURAa3HuI97FOjLTjA3p9in34rDPPwLZnZxp7lcs858Ym3sfLr+QY8/73U0oXToATg+yLC9p4XY0N7K/pueujvjLLcdnFGwIAKEqaWwkdxJIJOMoumQBJQXQxMx61NUzIXoaDuGjJCWWVkqIhhGszCYfkCvyNOzqXoV/taPmSdgDAgMbr/sN8fO+7KfQwKNnZ1StXoCIK0b33MgNrFLK2NrazPfuZcTvSTyT+ttvegSdE86yKoXHtFcw8L5CZ5K9/Tkrnwb2k9/zDp5kZLkzlQqpPURz0stgc9Y1cZ11+NUUffnkfi32ncgWM+VzL3buFc+KRLOeUjGhqBVv/imJdLBfQ2MCxKDfJ+9OoubNNc1dWstWdymD82R/8MVL6BTpkltl1kH1uxbXv+2/7S4TsRxFFFFFEEUUUUUQRxcs0Tiln34tZAa34XKr88SsVVPU/TzvCuAqiLBzznhKi7LtuaCRlspm2Ow1UHGs1qsZBy2YnEU/z7zoheVMiChZlKlIffg/Pp1SYgi/TlXKJiH5FyB4qRE9j4oK5QtNjTizkwVdURBtUZ8pcGipfNekr42RVfQSG0vsmtanrNJlQ3ZNKYBy3AFXdy5xqHazAyqqrKuL9V13LErzoHMLj6Tqs8EYFIHEVVJfgw3dVRxCaeEUFui9VVIWonH46kbqB4+TUd3d3obmRO/9G7fy/+c1vAgCGBsiJrKj4J+Tk+j6WLycK2dVDSbAdL7CA79f3soBp0aIFPIZMP/71q1/BcvEZTS7w5ptvBgB84hOf0GeEah8lZ/E7d9+NL3ycFuIXX0y+cedyFustXEAEYmyMKIWZUz366MNYsIDH6TnETMHWrTQXMUdxM8M7MTazaC9dk8L+LiIaD32HyHtSRfzZHPtCv85tWQe5+6lkEkPKIB5U0dQRcTI7OohwHhRKMqKiyzVrTkdWWSw7754e1UeIa5pKEfU96ywiuXv27ENcCOqv7qGsaXNzI6KY/cjlODa/5jUs3DOubV1tA/p6iDz/7Gfkri/vaOeHJDKwRSZXaf1+69atQ7+QwVWriEAXy5qzZIp2QNkBM5tbtGQhJoXK9RxmuzhP3HpDB228NaS/52AXFiwgImkGUs0ytps4wc8MDwsVzPDc+o8ewnVXspCwrDG9TkZY+aJJNLPTrFzJ/n50gNz+wC/hjNW8His4bmggimoGQ1ZQbtmJHTv3Y1Co4vikBClCRNLX98qAslHZsIb6sE+YYIT1DcfhHPOn72Pm5ehR9ve5ba045xUsih7oUkHjIfaz0yNvrVmPtIpiY1o7aYqneZTmfccqs8VSKGvlUSmwnbkmEOJMY8a+1ioFfXaqbGIf/Ey6lt9bGBnHGR2cW6o63kUqsnfXUvpyuzJTF2+ktPJvf/lrtLSwLqVjUTuA6bnREPfJUWbNrrmc6P3Q0Ahe90bKJ4+oHw7LePLpzRSS6Gxn9uzSy1jf9sJuZtcG+o/h5hs5nvxIRlsdEm5IixHy/GbOU801HNf7e3fjvR/6CwDAH33gQwCA41PMalV1H39xLzMJd/2AtWy16Wb8xXv4mcsv5TmYpHlZhc9F1ZhllVFf0LIAOc1NhXHJqYvI8rsiQvajiCKKKKKIIooooojiZRqnFNmPJyWvGVjZvck+VeBJmjIutRcnVLvkHzHB9L5rVs4ufNPfixkcr8yB/u+Kd+gIKUfRC02hAn2mYtXN4qnFdB5ZqX1Uq1OhfXO5TPTIL4hzVTa1HGUlxH13ADiS7gztqE3BR7s74+GXKyZhKQlO30ciwaxDNZTy5HENaff1fWXtqKvVKqpCUAzdsXsTulfrWE5g+zsXQcV27nzNZEE9S5EoixKzsny/DClvIi4jmkxsZgYmitkLU9tIp4hS1mTYLpwgwNQk22JSyPEL26kkcu655N+vPZs29Fu2EB0ZGh5DTw9RtLa5lOYzo6BrryGib3x5U5fq7OwM+ZkLFvA9JalZPfwwUXRDtzs7yTF+9qEHQ7Osv/yrjwIAfiuJQ09KDc1CIn/+c6oDeV48zFAcO8ZrTqV4rek021de6ipr157FeyH+Y1fXATyrLMB551GJxRBWs27fuJEKCiMniBTu2LEDv/kKFRPOPIvKKDuF0La1kd9tZko9PV26j89g3rx5M/63ZAml3MbHidxYmKSa41Rx2mlEUu2exE7piPs/J0zhpqmJbctqSlLJdDi3tLe3AwDOOJttaGiIaLah51bzccHGDZiQ2sd997Odb5dZz+IlRMvPOIv96/ARtv8jx/rRJJOu2jryeZ99lvU01raNsx+PTysyJdXOu3rZN294JSVDe7uYfRs6PqBzVd1ZIomnnmVbam0mad76gtURNKs256prySf+l6+wRmZ8KofcFK+rTzzotIj3Jkl9t5R7kqqJOXpsEEPDREvNTMkkgfNFqd1pemhu5b3f/NyzIfp/qJ+ZA5t11p5NFLV1DjN5Vu8yNTGFLY9QAalGKjylQkTVf6mimJWyoK91ibEiElVA/cU47o6YC3EtypIax33x2P1SMZT2jmuNYipuKdVVAmznyzrb+di+FHG1inmtrDUJdLzv3XEnAOCVV1Iy+VnVg522dDnWafy39dIuyVweV+b5tTcQib/ze/8BALj4yssxJqnpcWX/Vp3BscKy0bVN7PeTqhPtUeb7llffhFrJOX/gAx/g9Wl92tPNfv/YE48DABbJ+GvZ4nYMHOG5LG1lO25Ud6+p57jw3re9BwDwtte+jd9fm0FdHceIyUlm0TI1XD+OynhsnhSFJh329TovjUydDFq1jquJycHud0SE7EcRRRRRRBFFFFFEEcXLNE4pzhRLifMlflVZplE+yognpDTjkGMMIdClnLRDhexXxQkrBz4SthtVLYAvrr7vVWf83/jlqZoMPKEqJZk/WBbADHmSSe6oJrNE6wIU4JuWfImPgSrY46Zzb/x1q093/LB+wIy+rCahFBpViatvvHzj47tuqIRQkUZtIDjdORkWlJKPX66GpfG+siamsmS8fiP62/X6cOEHM+sGXJN4EeBv99O4er5bhSsr61haWZNUtF98qeI8ofS9vUQaDM2Ie0BdHVHEPlnZG/LeJQ76YXHfjSc8f/58zJvH9+SlHmXIRknIwPgEOYCeS6SjpbUZ42NERQztd1WLYt+TkWrA+vU81y9+8Yshkv5zcaSNj/yFf6LJkKlMGSL+7X+7Ax/5a3oKrFpNDuY11xCV7O6mEtHppxON+fCHPzjjfD772c/g9ttZr3Df/VRoMF7n0naiLZ3ihpoyTs+hbqxYyUzECZmD3SgTJuMuW41CfW1NeB9Lyuh1LmsHgFA1a14bkSnrR+OjRGeWLV2C/iNENs+X/nufFCeimN1Ipjhu9/URcTNvhQcffBDnricX2NC397z3vQCAf/gsvSqOS3P+fGlo//0/fAaXyojt0GHy/TdcSN7wo4+Q5xvXPOGr4qni+5gYJ3J47vn8PsuqjU4w07RyJbM827btAQDkC8BFF9AD4IZXUT/8xz/8IY+reaJFmQpf818snUamiW2yu5/8+s5O1sRk81K7uv0uAAgVpz71d3/L731hB3oOsc8/+CgRybLmnSZppj/9HOt4EsoiTuWKyOXFyW/m/JBXhtuSxko8Y1LeGHCc8LunJvi/yQn2iewUx55DvayJqJTZh6rVALVp9tunn6Zx0dgoX1t76TWIYnajJGTfuO62PnFjRQTKiFZDBoYMQo1JIZ8fTwuFuA/E1T5NPWbJImajTfHGsk8pIePNzc3TmvFaLH3zG/SnuPQiKpf17ecc9oZbXgMAWLa0Hfv3s5bK5p9Nyka//vWv5/nLP8kp8NxHB4YQb+N8Zu207yj7dCrDc1m5mn1w08NUqpqjc773vt9gXhvR+dOVoV2nbPgZGzgenHEu63K2Ps3sw+OPP47zLrqM35fn/ZnTwuyV1T5YzafncO70Si4m+jkP2frN0XjW2szPVoY5N2elWNW6pCVUhdz3AmsMGusiZD+KKKKIIooooogiiij+R8YpRfZ9ucoiIUQ8of+XnVBuxxPnS6A/HFX8G3/c9LirnhNyy3wDpKUmA0fV4trKeEI3E+lUyG03V7hYijvOtBA8U/Yp51VFjnK4I7OIm5WttGNjQjuNsw8nhpzUGypC/Y27Xw2Vg3jSntITYUW76yAuJ1vX3G91ITHx4EzhwBFC6pUr4S48zs3edA2AquD1NLRf9jGN2Jsaj92vim//N7UeIfteEN5zT5z9k25NFLMYgwNEol1xJefKc35yfAyjUoQxHvmA3Cvb28kj7zvcCwBYtYqa1bv37IPt7ZuaiaBZNuDYAI9VL1RmSNzfZCox7Q6tqBMf2ZDviQmiL4aIl8tVbNq0CQDwwgvUyD8hZRFTSFm4kLzd9773T/l9wwOYO5fo+MMPP8hrnCTqv28fUVBzOXzTm+jKa9mBL/7zF1DI8+/Va6Q0IuTpwAGiQFbr4iljlkwmEJgPhrJZK1YSHd2+47kZ12NqQ6VSCWXV7ixZyvtmmZEAHKys3qegzEm+ADQ11+ka+VvaMaKY3bDfa/FiovcT4umuP2cdeg8R7XelyvaDH1BZ45e/Zs3IR/7mrwEA999Ph+VPfebv0a/akQcfkUa3xu+EkMk1UtSZELrWe7gHbc1EBB96iO1/4RK26dNOY7bquefYttavZ5aqXC6HKlB3/se/AwD6j7AvXnwRswPd3czcpZNE2hvaWnH8BDMRjfPYn85cT3SxAmYSzt9AV9+dO4nSP/EE0c+3vv1tYe1Lq1ShntlKXwI/JgdU9R3X4+QcuAHmLxGnWXrh6nqoq9VcpgnkxAmijuvWnoPcBPvXgsXsK/v2ENl/y9vIUz7cx/5wYoT3eeeOXair5bjUWMMxbdMmZlGYy4tiNmPRfI7BZWnpG+cejh+q8pmCjo2Vpm44doJjf4tqVFpbmtAsPnqbXGHnKrtap9oPT3NYscCxMlEFFi7iOdz5nTsAAG99/RsBAAtUU5aRYtS6M+hBMTY6gkXziLQ/9MADAIA/e8/7+D3SqT/QzTH/FXKJPzY+jOc3kdd/5vnreA4OG6wpab39jXR2t9qaBfM55h8ul0P1t1F5WdSsZt+FPFWgz6y/hM7yz23dgbu+T5Wd17yJx12p+h6ojiVQ9iGQZ0C8tiacOzypcWGcdTIoa42Z5Pq0oYavjxw+hormGXNnb5z3+x2nI2Q/iiiiiCKKKKKIIoooXqZxSpH9QkAOnkADxDOq6q7GURU6ZxxjXzsdV0hKPM0doiOeupv04NYQjfAkCFMRl76knannmna/UG8vjsA3DXnuslK1PEZJ3LVcVikFz2oE4ghgnDUpAemuedLz9lxzlNPOLXDgaAcruhtcXVcsMA19ofPmimvbZtdFXCiOISamJhRPzFS+CUpS/ShWUNaba6TOUjQn1ZycefN8bjS8qlsJtXWNr+dr71e22gcholVdrwMXrrhkQSKm67C8QBSzHc2qIynKzfWEED2/UsbcOUROrr6K3OJvf4u89UDtbKFc9Qx97uzsxKQcn5ctI4pdlsfExZdeDgC4++4fAwAWzyeyMjp2AjG1J8smmdJMTQ2zT8kk26TxOS+/9ircfffdPP8Woh5NTY06/xGdE7nGX/sa3X2ffPJJ/NVHPwIAuPWtt844x1/8gs69X/7yVwAAL+wmErl0KVGhuQvmYWKciOKw7o9x9s9eT35lXx/vwZQ8KK591bVhxqBPtQ1PPfWkrpN9wfSbzadgeHgY2SxRXFPjaRCaZPcgn2dfi0npq66uBp+WG+Ndd5FHfVgc8ChmN1JSU3M0HiUS07Ulx/qJRFo7L0n1Y9sOZp6+fQeRxbe87c0AgExNHQ72UHv/gx+iO/Pt4hO/Ttxga+PnbCCqnqhJhtmudC37jOnrb9tGDnpTC/n3R4XmT01mw3qW7m72iUULmb07McHMVnMrn1t/GB0dxaJO8uHpgg4sVtbLlOZaGzluTJwwPwi21xPDIzj7TGYkjvQzE7JzH2tiRsWlt+8bGuNY4bhxFMTnN23+VJz3L6/st7X/+gaijxPjOXTtZzZl/jz6CFxxBfXDj4gvfaSPbqWWFRsaGEQiTvT/cA/rCazcLIrZjxa5Ldt6KzD5Q/goax1VkAt7uaJ6LWX465QNXbSASPLi+W2or6HqTibBuSqt9zjKCpSEQncsZTsePzGOmI5347XXAgDadE5zGtlPWhcSYe/dRU56W1sbamvZtq9/1Y0AgAceoNJbk8b8zc8yu2VqXAuXL8V565gZeH4rVaze8Y53AgC6dnIOOHqEfW9ogP13fISo+rIly5AR82OO5sSqVKw8Zakh5gZG2Hb/8EMfxAP3UeltSOPBfGW263S/TLHouOal9FQOyRTvV0brYE/z7pBUf+qk2tXUyv40XjyC+kbe87gcsscPsW81MIHxXyJC9qOIIooooogiiiiiiOJlGqcU2c9L0zUjRNxVNbRTLqBQ0A5TDHJzoEVKSjrS9jWeupP7mDCdAAAgAElEQVRwEa8RqpgUx7woDr/AZtutmuavF4uFfCnPHORMfUcaqznp3ze2crdUdQME2hM50vEPtHv1hbRXxeEPjLPvB4jFuBv2THVHj07VOPNC9u0znnH2XVS1W6xo0xg62prbnT5bEtpacPyQX5+pJ9ro5XSOUjUqG6Ifuu460/akjlXfi4NpRHylYAy1jcUcxLTDdLRz98uRFvJLFeNj0grXPc8oy9VQ14ypLFFqQ77PPJO8wD7pfhtP3TTtBwcH0dTUMuN/hjIbV9mQyCnx5SvlUoheG2pt6HarEEBzlU2oT9x3369DVNxooFZLU1NLlLxtLj87Ll7iss5l+OpXifKvWsMaA0d97oWd9A/YsPG8Gec8KR+MSqWEsrJ9559/7oxrP3Bgn66XaFBTU52ucwQVqXHNUR1EXv2/QdmUKXGxTXOkubkJ9eKlWj2M3RNT8LJjvJg3/rnP/SOAaaTJ7n0Usxu795Cf3t5+AwCg/xjRulVrzgKkC/7Zz34WALB02RI9tgMAbn79LQCmfS1+ce89eOJx8u5b5pBzbs6ZpqZ09nqqKz0iNZBXXHoRmtUuOuTUbC68Y2Ns5+aCOyVXzPb29rCtXn75RQCm+5epl6xbR5jOahJ2bd4cKrpdcglVSw7K9ba5nnPWk89QGeR0KU5ddc3VAIAf/vhHeP9fkAGfy/N7UsrUxorKiDh8XqlwDFiwpB1DI4N6L/uv5/Hc+qViVLuAc/MVl18HAHjqqWfQrnu8ZAkR0Bd2MruxrIP3c/AYxynLgmUnJ1Eq8v4VlTGIxyMPl5cqBoREW8bW86bXGJa9ncqxDZSlQhZo/bZxA8dZyyDVppJw9JrVCCY11rdoPLX5IT/F15cuXIQxjYlLNF4GatfmQ9G3j+N3+1oi86WhIRSKPLcVqzjHxKUaZcpX1k/XnSeVnBeew7Feqc5NsM3HVAfa38u5RKKNWCPFt53bOZaMjI6jfhnH67k6R28Fs2iQNwc0N0OKbMjncdWrqfVvipJhikqZ5Yoyi+b70tHYgYzYHFanN0+KQHMWyt9Gbrko8beoqakJ1Xjccf6WaTFdflec0sX+pKgyRXFJalR0m2poCBffkyc4iZfUeALRaZINKvTQCsKNB4hL9jFwubB2dLy4L+qPq6pUbSQmpybgS9ozLUOCkopRKyqQjdfye3wNNL4TRzzN91pq2FPK2JMFuKU3fRmTVHJZlJVijZmrlaV7zBxMOxKjwVinCxCEbzVjrIreU5KJSRDKj0r2zamiqgV7STbVnhpPvcy90mkO0EXReXK5HCbU+PMFNsK4zLxcDcB5s0MvmdxmGtB78qpYy6nIJIrZDytqqpbZwW0gnZyohIsEy74e18KiuYGDkxWpVsxwo6YuNA+yQsFLL70UAHD1YlJivvCFLwAA4mp3tTVmiDJNZ7GNx/gEFzBGYTOjHc9zwsIuow1Zqri2lscrFm3jwPYci8fRrILB8XEOav2iGTQ0cIFdlSndggVMp9pCOwgSofHXkSMcvK141xb0k9nijHvSd+RQSMmziaWiCc6et+h8bCMR+BUklFo1LlyzzFiKmoDseVabpXKpiEndp/o69r9RUY2imN3o6GgHAKREeRuTOc2DD/4WfpW/21vf/hYAwPOi71S1CS2rfd71I9LYjhw5gk9/5h8AAF/+Mg2paiV1u6JD9LJfUub1j99Lg5xrX3kd3nIraUC2YK+v50LHitqHh3hOGzdwYb9161bUar657HLS8YZG2D6MtvOrX/0KwHShbl1TMzpWksbTP8zjVQTO3Hw96RDJDNva/Q/REOwPbmNR7Jlnn4WP/+0nAAC3/xtpSTlNkc9sJ1Vi4ERW58xjDB7vh6v5LpnQ/0RPWCwqXUMD783AcbZ7B/FwI3+wi3OLB847T2/mRqRJ86/jCvyqBFA3wgfe/0cAgNe+9rWI4qWJWLjx5O8zdoJtKYCPlOhgV1/JotNd2qh1agPXrLVYWuNhOukhHbf/8Xe1Y4TiCFpfzdcitlwshf3DTEIzEkmxBdDcRVzoQvNFoq4O8ZPA0/blXHwv6ejU/znm54sct6+94TrsFfVz23aOz+Uxzjur29mPHK3bUqJLz59Puo2bSGKBjBNb9D9oboNorFUTSTGjvJo08pLaTeseQwCySZqXJTm6eDGPOZ4dRdVn/2hrIyDnyAQVtVxbJvKif4ueHatLwlWhcX6Sa+Zh0ZDYK/9rRDSeKKKIIooooogiiiiieJnGKUX2xwrc0dQq9ZLQbijmOAiqRGTiFe7uKh53MBXxT9y0pCpNXtNzAGX5/NCQSo+irFSEgKeEyMf9DCqQqZYQ/XLV7KL5/0SSu7FYhmhMQ2NteJcCQ9KFpBSVHq5K0s0sp6tODAntbCGkHVWrttXpK8VjRbEIpTc9FMsmeSXKDayIV3QhuUMYAh9344iZJGnJjL68GZ9xY3xdDCokfSCtW1lWsfD4pKgL2uH6ui5XxSOIp1AyaVQrGo6f0ib0Pyp8ZbcsnZ0SXQTwkdD9D1Qt7pu1ufpCmDaVcZUPN6QRGKVk0zOUtms+yAI+qyYvlSS9Vi6FaLghkIbsG2JjpltGWUgmk+Fn4oaEi6qWFfXGUHnLOiSTSdQ3Gl1oStelfqn+2STU/MSoipQD9pETJ06ElCJTxK1R9ikIlHETcmtIf7lUQUUZqSYVN1XUl2InGddZlqBcLof90Y5n8o5GuQizAHpfa2treC8M7T35+FHMTlxzzVUAgK997XYAwOVXEuWenJxEocjfa3jLZgDA1deQbrLpaRZlf/FLXwIA3Pbu2wAA+/btQ8+hXgBA53LSAmrqiV5vfY5Ffq+8gSZYZq51ySWvwty5bAfrZaDWvoQY2/btRBa7ukhdMbrcsmXLsEqGPtt28D05IXff/Pa/8RhC+LOiUpy/bh127ia9YWCAtKObX81z+d5dlBQdOMYM1+UXkcrwyBO8zsbGevyFTOk80Xde90ZK2U6UKRc4/PSz/L5JnuOCJcvCzPIJ0ZFqajg39vWQ0nTatWcBAHap4HHweB8WLGC/GhmR5KwA0bmsL0RJaKf12bvvvgO1QnaTMtgcHuG4tLAdUcxyjCj7aEXezZI+BgIMHCPtZfNWZoB9ZZZNOhkaR1MaM1NJD0mNa0mtBxJ6tPHOxkHfr4TnYP+zAm97NGq1Z/OdjuWXpkVFwvWSwtY5XmCZBX5PohzH1VdwbFi3hlRXkzn/6U9o+piTwMP4GB+3b2fm75U3vhorJbVZ39qiLxLVWXOzsVTiop+7cOGrTZcLHPPjYpxALI90I9u5K9n1ercOvvpDoDl4SoaNNVr3IiGhDBEpChNjmFR/9GydW/v7aW8Rsh9FFFFEEUUUUUQRRRQv0zilMFP/KHc6bSr8TKZVOOt5iGe0SzToWWh8RbxfJ2PFr8Z9D1BVZYXxlq0g0ApPq0LvJ4RUxuGFKLmhioEQymStkPxW7nBrVFhSjQXwtSWyQkOToDKzK19IewV2TABCNatCXAvaQRe0gzN00M6xGnLRAgTaef5XZF887ZN2y47jhHqggVHorbjW0HldgyG+juOEhb4msTkwOj7jProJyyAog+E5KOl6YsocxNNREdVLFY7aU8yzeg7+TlNTeSTUH8yELmZIvzJJQ4Oy39aPWdfYEKISQ8N8rahCocNHiNAZ8u8LoS4Vi2G7DNunXrMMQkq/v7XVWMwLjbimzay8Ge9JZ8zAR3z5SiU00bJzaFbxl0kTQoiQnVugdthYV4caZRuqQlusgNb6nt0DRyhMMu7BEXpoUptmXGb3POyfleksm73Hr5pDHY+X0T0YK1rGgq/X1qTDv0t6zTIkUcxuPPU0C2ovuICF3CbNWioD7ctYzNd7mO188/NELH95LwvTb7zpZgDAw4+y2HZwcBBjyliddy7NeTZv4WfGRoj+3XPvrwEAzSrMe+e7bw0zVlag/chDDwEAzlGxYFFjf0MT62r279+P+UIVj8rEy0x8cnm25SlJFja0MHt1bHAw5OSnZFj0/DZmBVaqbqFzOR+nVOPV3MzPbnjFhRgSevm+938AAPCZz30OALDuHKLzvsb1yRzfV8iPIaW50VMN3OJFKr6dx6zHA/fTCG9uK/vqypUrcXyQKL/VJp5zHvnJX/8asyiB+tD4qApFUYQVw/f3s+B43XqZEUUx69Ewh5mqcWV+A61XprKTaJNE5BlnUiwhLZaCyZPXa7xNal5KxGNISCo95s0c8y1sPC2aSaPvhPVRZuQWs+d6dCztYyh+zAuFGxCWQtp6UCIiesE1Dn0igXFlsuc0Ep2fmGK/uPKyK/UWXs+FF1wIALj3tzTsyhcLoRStHb+a4xq2oFRVNcwwKFOFOFyJlxSzXLvG05J3F1qfUD2l1RfUNjQiP0qUvlq2bL7mTM2LU1qbxcOCajcsrvf0v2plpgHmyREh+1FEEUUUUUQRRRRRRPEyjVOK7PcOCTmOa2cohRikPDQIcc7UqEpfPK2SKZIkDOVmBIEPXwQmQzN9Q+WEuBld3pekV8qLvWi3yO9LyKyrVjbFafERTYGgWM6jql0bnMqMc7AqcjMnyuW4s6rmJlDrSEElz2uenOJz4wD7mMn/rUj5plgphyitWViHTtbawYVSomZt7sTgKLvgWe2Bx2px2z17Jh+q9zkxN1QXKku9qE5V9qbCY9KfgbhlvueiIm6a3RPXNR55FLMdpmpTrQrp0P/zhSmkkych6kLkDHm3dmUce9/3USjMVJwx1YGD3eTGTlaJZrbJoMTzvJBzaW3PZAPte07mWxaLxVC5Z9psip+ZmOCj8e/NbCuXy2FEahAmY2m1ICaNOTREmTIzfDH+fW1tbfi38ftDNR5TToChTYaKxKZ5lGEfthqBmepYlp1wXTe8F2G2RCor9n+7r/Z6NpsNf5+TsxxRzG5s3sz6kxFJ1C1fSVTy4MH9KJb5G2zbQcUZq7G66ZbXAAB++wBNcM49n1z7RCqJpR1sh3v3kx9vGZo/f/9fAAAeEw/e1W+fK+TRrmzCt771LQDAmtPJx3/hBXKAzzmHx7c21tDQgPFJIuhmrnh8SEY8i9g3u3vI8z/nvPUAgGMDA0in2e7a2kiAr5UE9d0/o3LPeet47X/0h+8CAOzewTqDb3333/H+D/w5AKCourMHH2b2oUUI5lVX0/yqocmUuFxMyJhxSioi+/b2AgDOWsOsx1FJf5qSlhebwtqzeQ5f+9rfAwBGR/me+jr2kfwU+1ujjIESCReuw749fyHHhZ5D/L1WrnwNopjdOKRsblpZydY6ZptGRoZRLHKcNrnXtTJii2tdNa2ww2M5AByT8RZTwZeMZtk3qW+2t7KpBcILueY2Rtr8Fq6JKsakYMRSSTi2btIizL62aquyFzE/eI4ualSXZfSGzVto2vad734PAPCxj/8vAAjf96obaNj19tveFSL6RRmYVg1ZN0VGq280VcegBFdXYvU80GNOWayyMtB2f9sT8TArGJ8p2ogJyWtmhfzXmSpeLI6Ezc126WWjtvz3ESH7UUQRRRRRRBFFFFFE8TKNU4rsH5O+aTzDXUxKjwCQTHCHX6udZlIcsFrx8v24dohhNfQ0om9a9b7QkcAXymnmWjp2CQl4LpHOeJLoSLqeO9pkjTRftUPMSZkmmXJRLPG8C0WiMCVVp5tequ3KikL2y6U8fCnYlEr6THG6Ch2YRgMTQuljuq54wgmRn0DoeWC75fB6TX1oJloPAI4KDKox7gArcdO71fd4Ml5wY2GGwhEXs1k8vhPimmV1fYGUfNyEM103oN8niEee5i9VFISIx6V6UJtmO07GPNTXzlSAsTZgbbFeqLP9OrlCMfzb+Mzrz6M5iukbT44T2S+KW1gulULuoCFApqZg4lKGpk9OTvMFW5QZMEOugQG+Z3Awp3Nk2zcVm3x+KtSonxA6OG8e9fR7e5h1MB50d9e+Ga+PjIxMZ69kPmfPU8m0zsjqdExZpxje20aprPj+TK5+2D+VcYzFYiHaXw55lfyenO6XZVHscVKo7Yv/Z2hVFLMbG8TVr5fmu3lJnLVuLSYmiMo1SVFjIsfnR/qpOtJzmOj5XHGVm1vm4J57ifZ3LKVBVucKcvM/9r/+BgBww6vJ83/gIfLVr7j6KvzH94kUWv+6/nqq5PzTP/0TAGDXLiLV1u7bOzrC/moKWZZtGxhWJkvX5ygL27liOZ5/btuMz8yZw+vq6OT3ujJ7fHzTMwCAZIqfXbJ8JXYfoF7/6+UJcMcdVP259c1vBACU5MPS0si+s3r1apSE6PYe4f0yH45f3/OfAIBKWX4ZmquPHxtEEAzqzPlafR3HkZ5ucvltvOpop9rQ5Pgo4PF3qW9gnwsMto1i1qMgc6aqivwK8hmpq6/BxAm2q2efo3rVXHmOrFC79sx0U+sQt+qE9UyelNccUy4rG8Jvpm22VvGBsurMSlaPpblF46wZj7qWvU4kgDDjquO4Zk7KCBUZTfUw5sPRqwPHqRh3TP3m+puZMZqQwcNSKW/drGxy1Q8QVw2pp3oFT8cPEX2dW9HMXgEklNH2zTDVzlXnUZGuf7Egn49ygDjkh2RZANXyOBXN/UkpVelY2dEJFM3QUvPQ+AhZJKpy+y8RIftRRBFFFFFEEUUUUUTxMo1TiuyP5rS7GyWqllTVsgsfKaljZGr4v4zQiEycaKahaeYu6wReqN1rOt6Oaco74jOLw1/2TFnDg+cqc5Ahkpis56NVSBvfPxCCWSmUkZ+itvfkOB+LcpwNrCK7PJNblnGcEPEMObsnUXUd1xR9xIOzqnLfn75Gvbdiu2QjZ5kaUPi8Gn5POUxn6Nwqhuyb7q0c2OIpVAOpCGmnHtPvEdN9DXzj2U2jto5ZmMfNeThy0H2pwnwdrC0ZF75arWLOHCKNptldrc7MsBjq7Fpdh+thXPw/c+M8NkgFkLJQC9Ocb29vBwD0dHeHOswn68Obao5lesak+ZvJZMK+ahxeQ8QXyRHRPmvvm5wcD9FxQ+z7hSIaj/qZp8nJ7ugg0jol3fql7YsxKut36zimlmV9IlTyCVV5/PA9hrRXTvIpsOuyz4THAkK1oeAkvn9zM7OEhuIXCoXw83a8CNl/aWJ0lGhd/zHquq85k1mr3bt3Y+/eHgDAwV7yxr/wpS8DAJ59fqveezYA4MKLXwEA2LlzZ6hS0tjC33R8jO3t2mup339M37NEDpubNm3C+edTdedptdU77rwTwHR7Nz8Iq2XJZrMYldPsoDnnqn1bdswQ/RqrD0nGQ2W1gpzhbSyw2CqH4B27mUlokBvuihWd4RgwcJz3YlLPP/OZTwMAPvzB9wMA7rvvFwCArgM7UdPAz//6QWYxeg4RFR5j8gHppHxmNA8mE8A559DZ1JFzruexj6xeTfT0UC9R1lahxpNjI6FS1ZEjB3XNkSfFSxXmaRBThn5UmSQPAdqUAauq3TTUkfWQlxJNKiO39MB48UBMa4mY/INsLWRriECpYMsK+b6PqrxOirlphgcAxMx9N2Z6+6phrPrha57Wa+Fz17TsjfRuvkUA9J65iXYAwEVXUnffvC2uvfEGAMAJZWJb5zG7VioVURHqXz5p3DaflnBe0GM8mYCnerBClseLx81XSvUpqhud08Kam3SiBoFqLR1lkusDU6PkMcri49t99vwYHDmDm1v17u27AQAr8d9HhOxHEUUUUUQRRRRRRBHFyzRO6dZ5Su6ug6PcrXiOUO2ghIQnLfw4/9dSz11QOmnOa9pFipePahAqGATaTXpShjGUvCJd/Nom7kwL+QAxof4xKQE5MamIVMx2l7C2AfFTY8PIZ4m6VHNEj2I+d6Khpqs47540V71EClPGu/dmcszCnaBer+jRr5rakB+iizhJH9zQyFDnH9OcxiAUntVnpGwA3YNyxbTajbMfhLtJO4wdI5Fhs0h7Sd0SqZsEJcTCHbP4y0GE7L9U0SK1GmsPprfvAOHvUDLdYkNU1Az6+qi20NhEDnNdXT0aG3k8c2g23eSzzqLG9uKFiwAAJ6R8A/jTGTX7PrUF4x3bo9WZpFKJULFncpLvNe5+21wim0WhJQXTAW9pRH6K1zEll91z168FAHR3kbPfvpgI6r4D5OxffOFFAKhWkBev0vq7IYxlKywQX9T4nHHPDTMfIUrvW82M0CRlwirislYrlel6FY0OhTyvz3j99lxfh2QiHV5rXjxxQ3mjmN1YsoQ1Hff8mlz7s9ZeAADo6emGp9/6iiupq/2Pn6e2/J++730AgFqpkBn/fu/e3Th9DdVknthE1Z2YxsK6Gvah/QfYLucvoquoF4thfxcRacuCGTpfVb9rrOc8ZG2v2XXRd5QIu/Xx7l5mIerV9029ybj9+/fvD//XvoTI4NYtRPRWn8msQPsyPh45wmMvWU6fge7DPciqVqWtlcc/f+MGAMD258nv/9hffwEA0NnB633y2CF8+V+/CAC4/jVXAwBuuImKRDYVLF/OcaO3q5f3qMHFJ//uY7wXms8HBsjhD3wh/cIZn9vC7Mrq1aswPkZ1krnKVEzllLGLYtaj6+B+AMCaNXSILcmhuVKtwNEY71ktpFBsTwi5U53pTeI6ATzNO6Hvi75nWnWQz823pFQqoRDwOy2rm0ixzbn6nniI3suXKZNBQpr1qVoeN6n1lOn8IzaTyw/HDSVuTBt/0QpmlzrWMqM3ISW45jax3dUXR8cmkKmZVrMDpmsRTlZXM6+AeMUHCuLvq3a0MMWx39UcHVMWoqVBGbkgBscmDflrmA1vtcD/F7M6prIGiSAGN8m5ZPQ416f7dx3A74tTu9hXQUaxailz/tiJZBzptP5OmKSRZIVUaOpjmrJijyb/GDgml8dXfC26q0pzLJbf9uRYMVwIpJRKrZSZmprKcZEBFa5kTOqzPAHPZ2PMiLqSUJGvq+8tFa0YRQY6pSLy+qEDnb81jjDMoEcLFHhWbOuHRVLWQaxQKTQAMvk+Z5paYMt+xy3qvUqXWVGvPYYrey/srDHdLzOiiGsBF9MGyDfptaoPRwsr14pQbEEVxayHGUpNTLBtmm19NjuBsUmm7kZE4zHqiEmQtc2dWaYzOjGOkhbutaKuFQt8PjzATey255jWnC/r9JETY0ilVaia0sBVljmcJDDjVT6WXJ5j3E0jUcO2lqhhu1LtO7J5DqpdWsBbsfi568/FyBBfq9ebBwe5OGh9kQERAMzXgHxMJkQTE2PhQujkotqM2iicmVJu5XIZFRWf21hhCzDbvEzbu08X7tp77PhWgGv0A/ud7P9tbW3h5sgmNKP6RDG70bG8EwBw2ZWXAQDqG0l7mTu/DQPDSoWrzS5axMXpbx7g4v473/kOgOlx9drrrsZTz2wBALS2MaVv0shNMqhqUTF7OsUJt6mlOdw4rltP6tnB/Zx8rWBvy/OUwDQ6XiKRCNtbRuCTmRSOjLE/zNNmYlhFk4f6+jB3Dhf52Umew1nncDFvBbs2lyzpUFF7D/vOqpXLQtfFpiaew6gW2M883cvr4tCAG2+k/OCrrrsWyYwAsYD377lnfgYAWN7BIuXBAX7WVK1zOR+pJMeqvOZVz+iqmr/tt5i/gP15YPA40knJHIqeVCpGBbovVVgbzAlcGThKWlr7kkVIabF9rI9to6+PBewNK0kQsTWLH0wLhRjl1AlM2ty+yRYxfH1Km81ivoCSTB1h9Gtb+2mcTUqQIq5NQGPQEtKsDfSN2zpKc6Nb1fdXtEbyPEA0JFeAckJ9bEp9OHmSkMOoZC4bGxvDzUsoD+rPlGY++fVyoYRyluNAqo4b6rwAH9c3gJfXHdN1B+OTqEgW29d8kZ3gutSpTEuVAtPSpRMT2RCIG9XxR6d57f9tRDSeKKKIIooooogiiiiieJnGKUX2y57kLUU1GTYTqoECHI9oma9TCkR7cYUk18sa2JGxleP5cK3w1ortTrK694XUHOxmanR+6wL4QicgY4OKZDSDPBFS+ZNgbIgFggnXh+ea7Cd3ZoXKzJ0tZNJSUUFJvupgSpSimCQ+47q+0LwHtkMUWq8UT6Vagq8iWEMQ47oXJVEKJrNmbMTnnuchKeTVi5t1Ml+zIuVYzOhPPPdCeSK0iU5n+LuUZE4UBEoz1/Pc+4Z4b5DMoKjNeEidEqIcxezHCRXeDA8zTbdgAQtck+kUfLWfkn7nhGgGk5KMTakAyyrDvWoVgTI1kzK3sr3+QD+P39AgacJxSWSWSkjVsm03ziXKMlE+DAD42Kc/CgAIHPbhiaxkAFubkM8ruyQ5sZok21dzE1HSz3zq8wCAnm4W6T37/NNorFE2YVjyYcpMTOlYLXP4PKFiwGxWpnWOi4QQIENJDWUxpN36nGU/4CZQqvIak5IptAJny6bl8zkdy+Q2qyhJIs6oOHN0TlacbAiRJRTK5SrS6ZoZx8lKLi2K2Y1Nz5GGsmIljay27iKK/tyOrTg+QJQ8U8OsymSW7eHEOPvKqtNIZXj2OX5maDiH8XG2u6yom5aRaWyRLN9BFrm3t5MSUCyUMZVj+7j7/9DcaoFQeSvI9VWg52m8nTe3LaTblYoqhFcfrVER5IkTHHsnclY82Iajxwf13ZStPNDLc1m3jtS3iXGec7nI9t+5RHSi0jAWtBFt3PMC6UkTY7xOsSPwgx9+VefGc29qaA0L+tM1PKeqrrP3wD08Z80tATh/FEtZFEo8b5O7NUO7isQe6lX0W6ryuiq+D4GZiMmwKJViBiOK2Q8rlD18uBcAsPq0VQAoq5nLcX4wQYWRE6J1OmzrVa0TPMlEunHAd20+Ujs2kQSJI5R99aeS5J0rReSK1l5UqCukP9C8VFfHNJPRT8vVElJaB5aKfC1o4BrFM8GI+MwlbQA3lKhM1spcS3KWNWmlsSz7oCxvU4P1l1JY+Bv4do7KTAlFN8LGi00nrbh/Mk9aX26K11yrjMmCVo4ldVqfjg8NIXtCzBLNQ3PbFuspvyCr8cHVHLZl34v3h5oAACAASURBVAH88D9/DAA47SyOX87i399fImQ/iiiiiCKKKKKIIoooXqZxSpH9kvhUnnhXpbIRu8oYPEGkzg1OKkrVdiQ+j7uylHjzcdeb5q4rU2D8WyuiMD5VtTRdtBET8m1GUlbIGjhC8kwqSkW4lcCHK1Qe+j7beZasplePJRX5FXwPsQbu3gIhrgWzgDYqmXj/VmhQKJu5RRWeZTnMHEzvccRJ9molLaqdcKVSwZR21HbNtbJUjwnZ9QXJuyp0zsQTIRLp6z0NssweFmpakIyn3WcvlUZJCGVFn3F0fVHMftQL2TBesMn91dbWYtuOHQCApUuJ7hVk5x0W5DbU6zPktpfKlVASzIpRjWseVybJEOrxrHHO52AqT9S/oAKumnq2mfsfJHp54SvWAQASGbar0exx5NR+MkJOrFAwE+f5DwyRC2pGXYvmzUNvF1F+V1klOxfj4Rv33R6NHz0xMRGirlYEO22/bsVTarPlaRnMdMq4+fy+OXM4vlg9gWUWrLi4u7sbZ5999ozrsVqBk4247PuDIAgRH8sq1ElCMYrZjfmLiUT/+/f/HQDwiksuBwA0NtUiqwK5eQso63r0KJHKhka2m63bKFVpssV1dXVYsYLSkVY0mBfX+GAP0bqiisuff/45vV4Mi2pvfu1NAIDv3fkDAMDCDp5b20JmtsYlE7p9904sWUj0tL6O53bsGDPK6QzbS1sz+/HhZ9T3gwrmz7eMEo+zajVR2c7lND2ayoorLGGJTMrqtyZx/DhR+pUreE7nnree31/Ldl5fzzFh4Dj7aFCJY/481jhYEafjKr0rlL7iS+ZZxbflah5QxhHiGMdtDvFmGuBZXaIfVOBbtlgZQceN5paXKs46g2jw4cPM1GaSJkzgomqsCmNMVK3OQqwA48kra1NwgZgVypr8scY9azP5AvtPyNkvF1CtmKS42AgC2EviqU9I8GBQHHrHcdAoJH9eGzNsbXPYh5tq2U8aVFBbY0h/MhNKXVpmG1WxOazY1lgPqhVAuAb04ev83TDbxPkvpeJhq0UwIYZKqYyiai5TYe0M5+jSBBH/rt0cb2o0L525YiXmtjGzPamxKanvq2iJ3n2A7JThPNNfv3nyUcxdyUzLsPparjxTwvTkiJD9KKKIIooooogiiiiieJnGKUX2TTXTZAOrof28jzGhgR6sUpk7pqTMtswYpK6Gu87aVBwCCULbZuPhm8GBoWoJ8by8hAc3xh2ZI+5+1RffULu6vBD9sknvoYLAMgZCHArieJVM/kd1BWbe4DkuMkKNipWZxjtms5zwrAZBu1qhhKhWkRHKUtFnStpReypx97S7dM0ArFhEqShevzavmQSPYdmAcnlK3yeJxExdeM3GmQt0rnldb6DrqcQtHQEEui+W5Sj4v78CPIr//3HsGHf5JmvZIGWasbExLFhETl9JaGS9Kv9NxcaUOebPJ5pYrvqhIkxaHPeJSb7HUPK5c4ku1tbyM4VyAUuXUuWkr4/KIvEkf++HH3gKALBjx04AwJoziIRu3HgBvvqV2wEAuUm2KwMcFi8iEhF32ZfnS+pz755uLJUVe7nItm6oecks1AUBmiJOSyszGPGEB1doUqk0E9k3pD2sh1E/cj0XaZGUk4m07gHvjXFC7XH3bl7fqlWr8OSTj+s+EVkN629UBxOchPKUSqX/YkZm3OUoZje2byff/vzzaabV2Eikr1jM4U23vg4AcLCL/PgnHt8EALjwossAAK+8mpKcJ5RNen7LJpwYI+e8Vkji1BSzXaedRuWbJYvYBrq7qFRSU1cf9qeDXXsAABsvpSpPb28vAGBSdS0ljcWLl8zDwLEjugLybdvmsl0//RyPsXgpx/H166etcurNCFI1Xa3NLTqXvQCAvWqzSuLi8svJ5b/qqqsBzW/H+4noZqXGMk+qQwPHmWGrS3M8KeVycIV0GhpfrZiqiNBMQ/iFmFaDEhzNkUFoUidZRUdzpVRbTEWlXKrC92fK/MZt3oli1iOhcdTWGJZJrUtn4BkqryyqmS4eH+Lc0mgZYQ1lMQQhop/ScU3hr6DaJ6szzAqZzpUKKIptUDW2gKD9vMbxojK1Nm4vW7YMBWVrSw7Pd1Q1XbVJPm+QZHuLMty19Y1wYjbusw3aGByqtikLkFT2NTAJ50o5VC8MQhVIf8Z9tPWb1UC0NNWjXtmFtOYWV7VkaGI/bVAWxbHsV009oKyzr/qeQSltHRvmdU1JeXL7ASprJZsaUdPKcWBgguNK1+F+/L6IkP0ooogiiiiiiCKKKKJ4mcap9aPWDtEN0WFxiIMqijJ9Gs9xN2lF1Yaw1dVxRzgn4C4slogjLa5xEOrNz7RmrmqHVpdR1XjCQ1LIvm87qar2O7Jktipr33ZfTlKG39OcsrJxCsV5T6S105XJQcXxUDb9VakH2RlaTUDZTB8Ev1SsHiCZRq04yFNT3BWXpLBiCH/MEH4ZgsXcBJyEOGqVGh0vNvOclSmxHzyPOIq69rzufW6MnDJfWYd0A49fclQ7UCnDrZUpkI4/KkQgitmPDRdcCAA4Pkje/UJxfp999lksXEjO7d69RPMMwTdupHFxDQlf2t4eIvibNm3Se9km5rQRITCvifltRNmHewbRvoScYg9sCxm18aDAdnuoi9mHoMLvue9XT4QGeHGZthnCPzLAc6tJsb+8cEzGLqefAVeITd4lOpjNss2/WOceeDHqN1PzHnhRlswM7V7EnQemtfPtEZhGtEwb3/j/AwO856tWrQqfd3Yyy2EZkpMNxQwhCk3xXnSedg72PIrZjYUy1TIU8Ac/uAsA8KZb347t22g6deAgea/rzyHSPTxEHrxlx6y+5bzzzsWAGcvJo2HePBlYbaUJVEZqV/ki22lNfRL5gtSfSuxPh1XbYdkdT2N+ZwczWi9s24Wzz6Z51wll4lramF276grWh5wYl5JQiu2+vbMDVZn27NlD9H9qjIjrkaNE60f4FAcP0UfgxCj591NTI3BBNLG+kf154IjQQCnOJcRfzijzVchlEWhOTCTNv2Zm/UyparUr8oxxqqHKHJQNSIrj7GBm3VCYvS6Vwn4UmgjGIizypQpD3usy8tTxpsdO4+gXtf7ITnJdgB3kmq9ZxexWynxNYi48ofNeyOeXz4IMFiekSDOqDFkun8eUtOVLyuZKiCbUkp9WSOPzvYf6w37YsawdALB4AefBhlp+36CUd7pkNOU6DpYspCGjqWKZx4CN16k822Zc1BP7vmq1GioBuaE5qvkhCdHXeO7JIyCRqUNCCj6Qql1R6lmlHMeKuI0HMgLLTkyE1+pqXJkq8J7vkcdBt2pont65DQBQO7cFMSnK1csgr2MN56rfFVFviiKKKKKIIooooogiipdpnFJkP6iYW6wQttBmLY6SVXznZlrdV7UD9RKmaqPdfzyG5nr+LyUk2ovN5OoHQmWm1XoqcIyHLjSnZBysuGnZ82lVu0DXdRGYKo7p6nvcGcZriOCE+q1CMlGuIpcbm3EOvimCCOErCmUtS2vWdnaO4yAnlZ2iof7S8S8rP1DV+cRN2SDuwRXqUpMgLywnRZWSHIG9mOnQqjYBQYjAVnXtzfNY4d4tC/fsBHeOFdUZIJVBXuefL/K4EwXLe0Qx2/HUM08DYBYLAAbEmWzv6MTRo+Qfz5eax5S4kEvEfX9m82a+LofKvft3h/1i3nyiI+kativT8Z8nvnBWOsG16Voc6iFa2NLE41RNpUoZhI6FVHXoO0QEoqVxKbJCycsltv0V7UQyJ9WeTuskatl1gMogx4+OhDxKc2c0NNQeDfUzVN6yFLlcLkRqQhdhvdd09u25ITsA9ZABIKaxw2oE2tp4b0wr2d4Xj8dD9L+lpWXGe17szPvi74vFYuG5hXbrL0L9o5i9iMszxDjvb37rWwEA/+sTn8RrbiZnf8OFFwAAnnn6WQDA7r3ktt988y0AgDPPIsre1taG+gb+bps2PQEAyIlvb1z9krLUc+cxS1X2C1i8lIoa2Un207pa9ltHmdGcMmfH+81pdxUuvvgiAMCw4PiBAWYbmqXnv6yT/XtUbW3NiqX48V0/BAAc7OIcI3oyzt/IGoE77nwPAOB/3/19AEBtI6+lc/kiVMvsg3Pmt+jOSSVOni1z5vB7Pc0x+bEpxDXmV1xDf1XjZXNxVW1amWIfQYjsB5rXEvKbsDnf8e11OYf60/2mosxBuRJx9l+qMLQ+JUaDa6h6uRwiwKmamZnKvBZHff3MgKXFukjEPFT1Wl5Zz6kpzkd54+qH6wXNHwhCTxPL8hhbwNDyWNx0/A09T+PoOI/X+wxVsIIqna5rpOw2Rxr2No4319di+05mv+tlomTZ2yZTrpNCWr0ytfX1rPfJZDIYn2AfSwi5r1UmxMb1cliTpboWxwkV5bwKX0smOEbIMB4jJ4jSW62LE/MwOMIM3sHDVOPatmsfAKCkX2OvVJPmLu/gMaYmkFAfOyofhBrVtf6uiJD9KKKIIooooogiiiiieJnGKUX2k9q5WeV2NTBdagd+YMi6XNWEblfF4YXHnU/ReIGei2qVO6amOu6kapPG45VOrCdEosBj5cpTSIhznJMiQlH69HHtkgrG741zG1ZGAE+IYFzouJfkDiqZliNwjLs840BnS/kwU+CYhCtM717qPNrpZoW0GGf5/7L3pmF2neWV6Np7n/lUnZoH1SCVZMkaPOEpGAewAYMdYuh2SAJ0hqdvEpLbSbrJcLvJwM190t2ZSW5IbvpJP91J4IEEQhIIkDAHbGNjG9uyZNnWLJWqVKWahzMPe7g/3vV+Z5BkCJHUUP2tP6fOqX32dPY3rXe9600m0ljcWDP3BQAcjYDoKtJV/30yKY5rtomUReJ1qFd+gucT6io6ChEo2xiT/916w80AgBdmRcdZIpsUkTWLZbqwQl1/gY4jtaA9O93iyiHOaE1fnzxnynzt2bMTi4vCAKhjxVky8A1m+E9uFy3j/Lz8lrGYh65ueX61aqc6lmguwNIFYS8TMWlX5cBHhmyHz+qxi0uyTYo+4OVNstiOsCP1ioe77rwPAPDMUxJd2FjRqrFyPWusknv+nFzDPffcgxeOih5UWb2hIWFJlTVXfbDq5FU3H4vFDKOujJCy6Pq57qPVB1+Z/J/9mXcBAD7wgQ8AAE6dEh91jQLo8d797nfjV3/1V9v2r/vrPEeNOjiOY7bRz/S4FlcWx1glfWqH9G9PPCEVdSt14EN/9bcAgF/4+Z8HAGzS6WJyh0Sc5hfkOdQ6Fvfffz822AcrQ73/gOiUN1nVWj31X3PfPQCAhx/5qvmtP/1paXN33zUFoBlFHjsg1X19MuLpdBpfe+JRAMArXiHRLs2bOT8n16OM4rGjcm6bq/M48aKcw8FnpILmibNSG2I1L+f81NfFNWqjJNvNr8o+j506iNtvuwkA0J9jBVufjCu30bo2CY4xqXjMOOu42tXT0U3HNpfjesgIdBC15MswGp3wyBJzF5pPB477ouWndpv9nLprWVx5rK9IP66/k/ZLlVIZMc4HcloThD9aqSzbrJ2UPlL7xmSi2QfXuI32hVpBucHIQZ1KBy+ZQopqDUervFOpoPMR0EVN5yn1IIDL3I+Y5oCwLlOdOQLTS+KidYJzmLBexVCXzM+yHCuVydc6M1mOZcrojw5JVGBgYADd1NBrjoMZF1Tvb6IbcfP/VJzzQVanzmVzPH8Zs+YXRTmxtikR9c1yHkeZT3ToJckvirPK9gDzDeI56W9q/C2S3VlUIs2lYVVhLVpxGVzTyb4m/6j1ZsjiCWHkIGKyqVpx+YFaO8opXuCNCx0JDzmOYx6oWl1u/Eiv/GhdaYZPeGPqDJe49RABJxw1dnLa2bm8YToXVulEGISI8QeOaREqWjiVAi06wUQlSoBqAFIxnexz8aAJHZzU60Qu3tB7IuecySRRKvJaI01Q0aQQ2b/Ha1AXvyCMTKeq1wMueOIOk2xrmlTVnIiEKgviImWBxSuKtDt0mHgcsTFuFmumQI3Pc2gENjh0tRC40jnMsQPTzvArj30BmxsiKdFk9+ER2nqxCNTcnExgNGRZrVZRLMj+errZ+VTkdw8b8kxMTUnxj8VZmTTUG2Xs6JdFwwylXdmMPBODLAKiVp837RPLw6NHj2J+RsKKA72yiBgcFGnAOVoQlguy0N6+fUr2PTODgLZ7anGopdoLDDerJEcnI+vrck/6+vrM5KBcZoK+Fkvhtp7XLj8IQ8d89oEP/jkAoFZXG0FayTKBv0x51G/99m8Y+ZNKe7JZyocCdvgJLQbEdhUExq5QEz114LK4sphZlOdhaq8Mzn/9SZnwZrMA16eYWZRn9Sd/9t0AgH/4BykMNzsrbWWQSe9PPP11MxG4fr8kvWmybY5WnM8cEqvP8ysiaVhcWcCdt0kb+J3f+hkAwKMPPw4AJslvbJvs/8KihPEntk8a68unD4os4etPysR9VPJ0wTo86JVmjbc9+Gb8wk9Ke/qz//F+AMBrXy8FxG65Sew5Fx+Wcx0ZlbZ/6AWRy+24bjueOyLH2ceiYeODIksa7ad8h1JYldxm0gkzXpfKco9DTb5VWSvlqzFOeGJuAq6jxBQJq4jjK9ug2jn6HP8QAhGJozoXAj4JP4srjyQlMkYOSblNtVo1SagVFofSibzKFuOcKyW4aNaJLgA4XvukOMl2lOR8KPJUlh03iapw2o0TlLCsMhG9qpLLZBKe0z7fiEj+hjFaM+txOXFPeBHilKg1+OytMzFYX0M1ZWHfnOZCojubNUm7OtdUW/cMt9ExoKtFApTLSHtPRHIuajZRqsprjVLYfEXu38rGKkqcc6kxzAblTtV1GYv33ShE7CotgXdMTeLUtBhcLCzJwmaz8PJmKXamZmFhYWFhYWFhYbFFcU2Z/UBXL1xBVSkNCF3HMNsBbS1JZsOn5Z8meCRKlKWsV0wIxyNrFnPbk998j+FHZbCjOgKGJDWMGPB/uuLUkskOk/3CStUUnwrIrGqiir4GjibKsjhDOoV6Vegkx2HxLrNqJdPPO59IyucxlTilPLgMeVYqmvTCFbbKETRvSctaOy4CU/RB9t/VLytOBHL/lAmtlGRfsVjMSKf03j7yhBRKKjLsvGOH0EvnF2V1ubi8ggbDC2mu2Ivllw8dWXzrcGO0oeXznUook1ZDtkvaiVradaXVBlKekVFSgxrZ6+7qMUzJxto6t2UhqbTsq1yU/2vy3PjYCOZp51ctyTOyfaew/6fPTANostnra8LIZ9I51BgpKjEK5LEAihYZUQlOX48wIKtrK8YCTtn43bulAJfao2kEQVlSLRrWmvDaKp9pPTftb1RuAzQTrJSxUVZJLUxNkr+JErgt0QU5SU3Y1W31VY8TBEGbdKj1uxZXFjuuF+br597z6wAAze1c2gSGxuQZGt8lzPcDb/kpAMCNNwmbfe89rwcAdJOle+qpp7DK31oLqM0ty/PW1StJfbtZXGtpXULxe/fvMzKgwrowkfuvl20OPiN2nSsLso++QYnCHTp4GAePSISA5KXpxc+L0k7rNOJXf+PnAABjQ304feJ5AMD3/asHZL9k+4pF6advvkXkQg8/KdGNTE6u68LiecQZaTp7Ttj+cl6e4eu2iVxgaVWicuN9ElmLpxMocZtyWV4jjVYzwh1TtpbJ7sl4CjFKPz2Oa1VGlINACyjRkKOhbbPlfw21q7Zjy9VCpwGBvvb09Jg+Noja5ZBxyl021qVtqIS36leNIiKdkn48Rbl0gvMEtZlUNUHohGiojBhaBFGen3iHKYPTYq3u0pNd++sGI0MBKzdq39uMEjjw1adFpdJk/3XMiVMFoXahmkSeSMRMgcSAkqKEJhEzednlPLHGKNdasYZiVdpjSo1cOH/MjkhkuJuqjiStep2NbqQ4DjlUuKQo/bmwLG365BmJ+BVZpGzmwnn0D8g2Z07J//ZcvxMvBzvyWFhYWFhYWFhYWGxRXFvNPpON/LC9nH3oukZ7HKf+K2BZ7Sp1ezGyCFp0y3Nqplyz6qq0QFUQiK5ymPZp3dywXNrAYtjOjkcmWZjsKRMyVqmVGhkeQ7muCUNMOmHYQVeMsRTLIsdV61ZGjYlWXSlq53X1GCorIq+pFBn9hDK1MeSZWJXgqnFkm6zYfF++c2F+mceXa0klUqhwxee4cr+qTCTzqJ10WXsoxT/q9QBVrlp93r50Ru5bDy2i5uaFtcrznkdhDD4zbfLUwaXisnK3uPIoVoTNntgxBQC459WvBQD8xZ9/CKMjLCIUakK7FiiRh7JW0gQp2ZcDD3HqNNOpXNtxksxFyWakjS0vzHBfJQQMDcQZVVhbE3ZSNfUNRoGWl0SHnMl0oc7nSiNvyhql1eaNkbA67TvHxoaxzudV29jhw4cBXJxApjkJt9wiCY21Ws1ErZTNUUaosxCXtrEgCMz+fDZiTc5S9l7ZGI0WDAwMmOvQzzoTdPX/agEai8XM/3R/em4WVxaff1QScuepz9+7VyI056YvYHZJ+sa//NtPAQAGxuQ5KLCtvPVt7wAAFFn454/+9IO497V3AQCWN2jfyn6zSgrz1FFJUtxzQCJQC0sr6KcV87kz0n7mz4iW1uf4MXdO9P3PPy9JeMlsCpQLI5ej1SXzaJhWg/f95i8CABrs39PxGHZsl2sr5MnCT0ii8WkW4FniM6zR4wb76nOzZ0xuj1+X6+nJSv+tVqLK+C9My7lPDk+hRG12qSJjYoHvu7K0LszJa6POQk1dfRgelf7JY9vLZrTPkXu+sk4dteYNwTHWvHWaZwwNqT2oxZXGCvtxY3PJEFK1XkOFEWBwXlVg0q0y+SEVBX0DkhPS09NjtPpqo+lxf3H2uRrQHBmSZ8WLucbC+AkqCr77dfe2nVOcTP852jpnMhkzHqyvS2SiyCJaXkexQu3fnShCndr8BqNL27dLFEsjzCEjST6VGtNsA5Nj40jSzjLF61OL0iLzGeIJJikzUbdQKGCArPxLp2U/g8MSQQwrLODI+R3ry8FNxlGqybEL7G9SVGCEauzSMaZVykUU8jI/2z0pbS3TUizyUrDMvoWFhYWFhYWFhcUWxTVl9pW1M7otasZDwDCIupIxWlwy01psp+HL54VKAwmy48ogquYqCjWDWlZL4710yYgchGRZ1CXDc4XNDLTEMZn+JFnuQs1HSL1WnFZR6j7QoBeZKsqqdPQoVYoYIFNIkg9JRiwavM7IWGNSG8y9+JFvGJlKXVae07Niy1Qq0r6vLNeVTgkr47gxuIwcwFPXIurtohrviR6HWra4hzirPHj8rFLlqrEu51JkGesq75mDpHH/1HsSWsOEq4bJXaK7TzM6pGXpf/T/+GE8Tibz9ClhEXu6xB1nYlyiQBfywiKGQVNPrsXhIredhdFogMNnRZlAL9YsWR9Pyg+fZmGSriy1isqK+MwRQYRcjg4MiT7+j9EmlgsvsDAKd41MmMIw2Q+f7VwdDEwhMOYgqLvQxsYGz7VxkZ2lMvmdr7ovz/Oazjl0flA2SZl4ZefVlWV4eBhra8JsaiRB+7POSIJqXB3HuUirb4tqXR18+WEpQPfOH/nBts9Llcdwngz7KVq9jmyXNqI2q+/9z/8FALBGO8J0VwYnzopoXp1oVlm45jztaQeHRNd88tQ0AKBargAsbuWwb5zmPjLUMatrdBfHlomdU1gryP7OTMu2fNzw3vdIYSx1sFtckMhZODGAtVVp20dPiF3tTXQB6mcbmWcewVC/nNDpcycBANft3oU03T4CtvEF5sLkknLggbS0w9lFOnysldBPm9FEXPsCeU11sy+gQ1GFBTFrfg152ggl9drZNrq6ROucoW11FDG/p7JhxuIe9jEjwxKxsLjy0HlIzNXCVeqM4xj9veu2s/QZblOu0BKV/w9dz1h/+8yzCNjnh2of7srnS8vy7Oa600hT1RAxOr2xLm1M87QS1K+73Ge1UjQKEHV1UnfBsMqoECO12jc7joN4QvZXqMk2C8wv0witOuxoYbA8c84KjUhs1NHsB1xep9Hyx2ScStTkPhYLRVQ4n5pjLk3QJdehY2c9qVEPjsOehzDGiZQ6WjHnpahFyuikp45yiZgLT9uyr/ePiT+XgWX2LSwsLCwsLCwsLLYorimzr8VEVCevOi+0MGC60nfVaYY6pKRqssjsh2EdFUrLimX5bpyrx1ic2fz0ZNWCWb3dCYTq00r9WVq1tKr3p77LIXsf+QEcKBPKr3J/tYZq+P3WXaJaraKiGn1+yacPrC6vzOrZ6Kx4XYiM604QaZlqOhmQ/e9nSfMEizc0/Ka7kOYERKYCit5jZq3rtcA1DK+6CZWolSzV6FtOPWWduQKRE4PnKrXP3Vvf8KuG7VOixds2Kv7cvX2iCZ4Y34WPfuTjAIAYGeo02ZBVss+NUKNnsi8vBHxHbQn4DPDZDKi7b9TbWW3HDQ1rHRjWWpgOyjhR53erDWE4isUy4IgWszurkSe2T+bOZLtl/wlT5CSL7h5h+s7OSBQrl2svfFKl77BGHfp4L9bX11FmxEA1+6qlN44MUTvD73me+VtZeH2glSmq1bQYluYXucYBSHX9WpxFm4DWCAm1Pwgcw+RrhMQy+1cHH/vbTwIAFheFOXz3u8VLP2wEcOk2VWJEFJGwZAMDon1f25DnZ9vkFABgY7OEnn5xzjh7RnS3PsedBseHxWWJEvQOcJwKAzhsE10ssuixVszOiR0AgG4WxvHSwvDNrMyhUJJjM3iKf/8z/0b+YFtdvCAM++te890AgCAo4a67XwkAeOyZRwAATx98GgAwsUvyBypss15a+vXrd4unfqFaRF+PtCd9Ri/MioPQBpn4wdxo2znWwxAxurB09zD3pSLXlclorlp7jZdCvoZSWTXNdEIaF5Zeo/rJhDqvSPQhk04bb36ffUxYf3kNssW3jibjLvdce6V64GOT/anW+dG8LI/Mfhf75jS95dPptGH5xBUHSQAAIABJREFUtT814wYLsOlEs8bck1hPxljFdfE5qhaFcU/zOMqep6jCqFTrcGLq4MO5pK81jljUq64FDlkrKJ4AEsz74gRtk3p7NfnRuWaFeW81Ze/DZm0bToFMfqg6PZZDrTdExUkQImRNo3WOJWnOEx06S3paiIttLIoieOp4xOifryoV9gta1EvPNR53EFOHI7blhlYuuwwss29hYWFhYWFhYWGxRXFNmX3N7o4pm01LHBcuGh1VL92YMmJ06YkrQy0rqjhcoynzuUqtBrIKy9e4X0qYXIeOPuksUvQChkNXnpiwFnVqszRDO0nnoFyu2zD2vtYCiFRrxaq39DhPkl3tH+hFo6oOIXQEUWZP70GKbCdpQdU0hpGDWkQ3Huq24qxSm8jKuQ8NCdOreuyV1U0TdaiFrBZMPb6uBLX0tM/vVOs+ytSs1VlBtUpGv0YWq6ZaNqcZgdF77qiGLbLrxauF/ftuANB0HtDKyn4jwOCgMI+q06sxm3+Ypb4rRZZD577CMDR69Mhpj8ZEoeaeyP6zvfIcB2HVRJmqZB7UtaNOTaa6WPmBfLenJ4uAZbxXN0QPrJWetby6PpLqIDW/UEOOzL1q8tUHWrX56neuzO3KiuiSd+zYYfT3l0OnD34YNiMWUdTuya/bqBdz63koa6XRBvXq1yiB3t9WL32NWOp+rc/+1cH3P/QD8ocpsSx92uB1YyanQ9vM6ReOAgCGJ6UfnZ8Tdnv6rLh+7JicRIEa2UlWeZ49R4cqbTtqc8WHOZGImZyOfE1+8xt3H5DX/TfKphzLNliVecQbwbkFOWZamhwOPy+e/AHd3B564I28Qmlfua4Ezs+Lvv9ff//bAAB//sEPAQCKZAMfePAtAICFFdFAl1flXowODKHOyFiW1dHVhUqj4A1GuIYmhOHPpftMNfggkvOOZ+jcwhyi9RX5XDX7cOMIWSW0yj7r8GFpr9oH9PcJo6/uWp7nwCPzqfOEwDL7Vw0Vss3JqN2xrOH7qBnXwfY+MsWxP0cffu3/UqlES6Vk2X9VE1SMwIBuh+pxn80iznlgHyOkvursA63TImOb5lPVyyX4dJSrMbRs8qY65lHN/j1ANZDnU+s6pXlOmhug51HmGBSLM1cgkUSGDVMrq+u4EDP1Weh2yDbiNdIIOOBlGAFx4jpvZF0oziO1rkAUBSZ3zKU6Rce3DCvWdzHyHfA8avUCakyYVO1/hvkwl4MdeSwsLCwsLCwsLCy2KK4ps++R2XCU0ScrEgEAV4+ROmZQNxXjUtENVNMv+3KcOEJuUydbvUntfoPZ3ZWqsthcdSYDpJLKvlGDzBWV53JVlJJVZLJb3pd8v1mVrUOeHtEoNabOHd3CUoShjzL37xjtPFecaNf11kPVxTWrfMazsiKM6BBULfO7dE2J0ZVHfz7fiSNJY2a/Hm85GuCr1pqvyuYXyxFKZGIqZIVd1jIw7JgypkaT3My21+CMatosrjy2b98HABgeFnZsamoXAOD0qVnccefdAICTJ6blsxPCEF64IMx3PKaRHWr3GyF8ahJDo1Xkqxpf8YfW6JZE0ZS5ZLtUPTzZCv31PUbCwjA0Wsy6Tz96MhmdjjQNHqdWLmO3Vhs9KMymRvSUeVSGX9kkZQTn5uYwNCRORMryNHX4PDev3eHLb2nTynfod/RzZXBaGflOhx5lkTrZe30fBIHZpvN/FlcWv/1ffxMA8P73vx8AsOEJy75yeh7do/Ks6GDX3Sv9Kxid0qSL3bukfR176Rh+9Ed/BADwpS99CQCwOiuR2kwfnWImJS+lb0SY6tHBAeTPC5O+eOY8AOA1d4vOvi/HZ3VBIgjnzkoewGJxFbOzolN+3x+Kn/6nP/UxOSey5v298rzX6NF96ytux8yatLqzc8LwbxuX3J5bbr0NAHDk+RflHlCH3zsgTOzIwDAWlsXVp1yU/cfJYiZS8rq4KoxirUee03RXH9bWJUJXL8lrT07dhejaVmANjKzck5HhISCSc9xk5PHCkpzrZl7uY4luWyX6pMdbalJoP1Ep2poUVwtlOs+kKVuIa/g17pnaPBpX0f5TmXB1RgzILDf8mvnN1OWpXpNnQh3fEmr2w8q6QRCIg5V8KvuhRr9GTf3mujzzybT0u/l83uRc5vnMFbkPj+Od5iJo025ENZTr8qwZ9yDq/BHK/KZhNO9y/CTngKl0ArGkXDuHMnjMM4Oj/vecl6ozZNyBTwZ/cFByKzVfq8o8lrCukZKm5t7n/3zOxeq8ri5GFtQNL2Qb8YMIDfB3iHG8+QY++/9LZDz8PdA67MX4Q2iIX4tx6ETE04eRD2LMdU1yq89JSoM3qsoHuaRJhLQcrIUbprCXlg3fqMhZDLFDHGR4MckBoZHfBBzaV3bYL6n8wWezKDBjuFarmGQjHUjUHlS7ryDQhQk/jxjqcV24TNzQJI0YQznauZZ8lU7QyjRyja2bl5AOt8EwXYV2oEWGxHSdUK0DDT7sASdWDU589L6aibyGyHzfJADr74SYnbxcLeR6JIEw2yXPwTrD8X/wvj+BX5ffpCcnHcrE+BQAYNs2kSZo0R8zyfR8VNk+1KLW8TSs2C5zqVS4uE3EkUxqsTfKXPi7B9DFKQuuMZOoXC6ZUL0msBoLTK1kx2coTms1z/NwhomQw8MitVDrTX2dYOEgDc8uL8vEat++fSgUCm33TSfUOnnoLGQVBIHZJp1WGZ9ch4aOvUt0nJowpt/VyX/npF9fW6VTitAmtF8VOPz9LhynheWATJLHdo7i/jeKFOYzn/kMgKYpg6v9Gr977JljAIDX3vcqPPawJL9GtJYdm5LnstN2tVCQfYwM9KK3Txam3jaRnN14g8h41ldl4RFxEby+IRPq9eIKRkQtg6PHXgAA9A/IwmDHsBT+6e2TtoRA2v5nP/tJpEelj3dZBO8H/80PAQD+6I/+P/mcUtX9+/fLuY9IAby15XUM9olM7sw5SYRXiVMyKc/ywoosBs7MyIJlcTWPRo3F4lxO4Dw5foyFL0MSCir/6xnoRiHPhHouWtKU/hw/dhoAUNqQSf74mLTrXVN7jISvXKIt6AIXTbC40nCUnOG8KtAMXcdpkrId32nt14CWZNx6Q1XCzfHAJKWSaOE+td1UyjXEOz5LUBJT5sStwgmwSpSr1TpCzlFqVSV2aOii80PuI6JMG1GESIuPsvipvjcLErVup0Q1m5XnOJ1OXWTxmUwqmaqSZ50bJsznAfuMDNuU9i8VJuM3OLTElfiJAtQod9N7PsB2miDppOYPPif4TiZurHAbkLZU9K31poWFhYWFhYWFhcX/lri2zL6rhZ0EujqKeR7iKgvw1B4vbrYCWthBeOa9sm9c9MDXyAG/2SBTWWDUprBWRUrLONPCaa1A5pveZ3VaKa2RAk8nY6hXmczLpVFMk2zVBkmXtWQsy/WgKQMg6+FGWuSKCSUqKdCABRNPYp5r2EXNd/KYTJWmzKZcktWmFrdo1APU8vJ3V1ZWhHUW6yrTOrBc44qYMp8AMYQmTicvZoXLc1IOUi8virnGC8oHZQ9MzLS48vi93/ljAE2rx2SSIf0q0J2RCNTKsvyvmpHfeWqH2OwpS5/g8+4mPMChTRmtwVy1RSND4KgoJxJmw/MS8EyCN0OFjArU+LurHE3b8tDIoAn3+mxTFUoGtIiJr5EptXTzHMPUay54n0bYaFM2f0GsDnXf3SzcVW/UUGFBFSORofQvMOHZRtv/4/G4ScjV6IOy9trPqExIIwtyre0Mvl5PJ1vfKv3ptNq01ptXBymOBZM7hVUv83ebP7mAFwZEGpbmNsrOa9Tlvb/8ywCAF14Qdv3Qkedx5qgk5GqTmJoSdvz1974OAHDslEhlFjfluTxxsoyhpDxL102IrCaR0HFBjqNjW8S2MzG5DT/1c+8CAPzNJ/9KtqFF88SkXMf0tCQTTw1Kv57LZTBHqc1f/8XfAwDe9RM/AwC47433AwA2Ngr87jSApuws19ttotIZ9iWaCFiibKBcknMtsM1WfMdYH44PyXc0OT8im6oFgFxa665urmBxXtrz5rq0zXPTwtI///xhOc4mxw0W1twxudtEKZOetKulUEwGLK48VIapc7JASWY4TVmnkRi3SxzTahmphbOCBlyGfFUKqiyyKjJS7G+jUG1am1LJMFBbSdmmUtNihRop1TlfzMwL+VWjqnA9Zdz1PeVpsRANGpB087yN4ztZe+2Rk/yH1ietVYvmHlQr0qaSCZXehPyc8hFl3oO6UVU4av9pJqiUqek5+s2IQpL3TyVLLtuUz3Mtss/QxOogFSCe1igGC7ZW24tLdsIy+xYWFhYWFhYWFhZbFNeU2Ve7RnTa0XkeklxpGmujDos61XUZXWzomAxDUyxHkwE0OsCrC5gkW6vXzTKuwVXjxqYkgdS4SlVte0Arziioo84yywmynNmsMBz9tFYaZZlyZQnr9TpqDb217ZZQtTpZ+6q86iow0giA66KHeq1CURKsVFsd82SFuLpGK6kKGZbQQ6lY5/mrTZoWTuLqnKvJyNhoosmA0k7O1YIREUtBU+vsaJK055qVu28SRRg2sbjimJ+XhDh9vmdn5P3w8ChqtEkd6Jfk1ExK2LvVVWEt9Xf3TFG6GMJIczDaI2yuefbkNRFjjkjgGMa+7mvRNv7eZD80sc9oGcPQ6IDzZBhL1GDmGKHq6hJtc6JL2lEiGTNtKsH9KGuuUS7V8mv/sLS0ZK5PWfnOYlomaaxDsx+Pxw3bqXaJau+mjL++6ndrtZp55jVpWM+t817reVwKl8oFsPiX4yN/+WEAwAbtJvU3GRhMmWjkT77rxwEAX/7ylwEAr3rVqwAAh557FgDwxS9+EYAURxwZk778wQffCgD4/Oc/DwDo75HcriLzRG666SYAwPnZ0ybSe8ONopXPMgqVYxSqr1+iRRkWqVovbOJDH/ogADGPAID5JUm0nxyU70z0i2a/NyeFub781YeRJ4v5Az/4DjkXPofbxiVf56mnpMCYjkeHDx8BAHzXXXegxvPWCJlaGJ5fEK1+rkf6kxgZxiAKTfRZUWIkLe6oxplFiThmLq+uY3lBIgaRL/9LMUK367opObekjHHX75F71d83AOUe07SyHh2ZgMXVgc9+TYtRuS3RSO0LOw0VzNyL0V2fv7/vN6CjSWQsL9knmrxf5k1Szx5FzT5XjVb0O+UyFQsNNTNh5DRyEY9rDpl8xGHQmE5osq1+xY27CNRgRQuZ8lzNq1GNaCRDjr++smzaSaVIG9CEFmpk/mSZOnnOofzQh8/cTZ8RhVSM0Qjegyzz3xIxvf4ISbbpbkaUV/LSTiNG3dW2U9tYNawjwTlzLMP5LiMXl4Nl9i0sLCwsLCwsLCy2KBxrBWdhYWFhYWFhYWGxNWGZfQsLCwsLCwsLC4stCjvZt7CwsLCwsLCwsNiisJN9CwsLCwsLCwsLiy0KO9m3sLCwsLCwsLCw2KKwk30LCwsLCwsLCwuLLQo72bewsLCwsLCwsLDYorCTfQsLCwsLCwsLC4stCjvZt7CwsLCwsLCwsNiisJN9CwsLCwsLCwsLiy0KO9m3sLCwsLCwsLCw2KKwk30LCwsLCwsLCwuLLQo72bewsLCwsLCwsLDYorCTfQsLCwsLCwsLC4stCjvZt7CwsLCwsLCwsNiisJN9CwsLCwsLCwsLiy0KO9m3sLCwsLCwsLCw2KKwk30LCwsLCwsLCwuLLQo72bewsLCwsLCwsLDYorCTfQsLCwsLCwsLC4stCjvZt7CwsLCwsLCwsNiisJN9CwsLCwsLCwsLiy0KO9m3sLCwsLCwsLCw2KKwk30LCwsLCwsLCwuLLYrYtTzYl2ZKEQB4nofWV8dxEIYhACCKIgBA6MD8DwCCKGzbl35+qW07EYsu/fm3Cj3Hy37uBPDcOs+N5x3Jtfrw+F5ufcT3If8POAh92U8iIWsxv9rgfwIAgMfXKJTP0/EYYqEcL10rAgCmj78EAHjqqacAAKmUHG9tZQkA0J3L4PzMOQDAjqlJAMDK0iIAoFwuAwCGB4YBAKVSSc6jHpi/B3oHAACFQgEA8D8//JEre5Mt8OnPfu7SD9q/EJdrJ9pMtD1FADxtd6GcisfvupDXGNr3FQWh2X9ELqHZPj2z3/bjhYDXaNsWkfIQ8ty6YZyfs8sy7cVFZNp32L4P+NxVwBPwzfEdnkVC/3VV7vTlcf8Db7Dt5QriiSdPRQDg8IdMZuQ5aTSqqNQrAIALC3MAgFyP9F35vPSVF+YXAAADA0MAgEqpijCUZ2Xv7h0AgLk56SsdSD9bq0q/t2/nOACgWspj27B8//zsLACgO5UBAMwvXJBz4SPtu/JHLXSQG5TvnJuXfvnC4ioAYHNZ+tn73/hmAMDxl47JdyoV9OQSAIBnn30aAPDQQ9/P61gGAMRjKQDAmTNnAABDQ3KMVDqOuflpAECMzckP28fVHTuuk+NvyL3xG4BfrwIA7rzjFgBAvSbndvrsdNtxRrfJvZiYmIDfkN+hb3CA28i26Yzck4Dtu7u7GwBw5MiLuO46OfZzh54HANx8880AgF/82R+2beUK46d/4V0RANTr8jxr/55Op5HLdslHfDbW19bkdX0dANCb6wEA+L60kbDhm21dHR/4jDfHAvC99NubxQJSKXlOdd6UTCYBAF1dcvzV1dW2cw6CwBxT95/h85RIJNqOp/+PxePo4vkODg+3XdfCgrR7ndM0GjIGVavyvNfrdXNOut/O43WeeywWQ7ks/c3q2gYAwPPi5t4CzWde39dqNSwtSftfXl7md7xL3ke9ft/3zWd6Lvr6sQ//1SXbyzWd7Gd5oyIdWSN9EMKWiYD8EB7/F7qyrRN67d+FA32EPEcnFfLe5Xed9jmAjOg6Meg4h8u9dx3PnFPn/6LW/QLNCYoDeC5/LP4r5DnGObnXzk4nRDp5ikLHDFgJdshp9sxryzLAjAz3y+fSVnB++jwGu+TB8X1pvEeOHAEApFLyAOgkP5uV7RYWFnDjjTcCAM5OnwYADAxIx6wPjTaKWIwTLnjmM51QpbIZWHz743IT/Jf9zjexjTYBnZo7jmOaifbwnR2+aUaOY17DUP/Wtha17yQM2s/KTM6ji87TNWdF4qCTKMA1ntlbXHV84QtfAACkM9JXbmzIBGVwuB+zF2SibgbZjEwmYjHp53RyfOjQIQDA8OAQ6lUhPNxI+tOubhnM6yReZmemAQCLMyfAf+D4MZmQv+XNMkF/8shXAQBv+wGZjD958BkAQA/72VyuF2dmZwAA+bxMOEYGpG/v7+oFADzyT58DACQ8Oce+3hxmZ+V6JiYmADQJl1OnTgEAdl+3FwBw/fXXy6k1agCA06ePI52Ra962TRYAc3OyMBkdHwMAnDkp1xM50uf35QZQqcjkpb9fzu3xx2QyvroukxltxydOnOC15DE5uR0AkCwkea05AMD6hnwnkWhfkPT395uJzejoKIDmxMbiyqMrkwUAlNgVbm5uAgDy6xtY5DY6D4hz/NdJ+AZ/Q4UHp9mX8zOdJ+gE2g+k/17hM+P7PoY5+dYJrr7XybfuQ8lHoDn51YVC6+S39VXPJ55I4Py8LLb38rPxcVmU6nN24YL8f25OyABdAPX09Jjj1GrShgJeh77XbfUc4/E4Go32BUlvr7RlXRicO3eu7TpzuZyZ+Ou2uj/dh37XjKVR1Fxs8T59o/ZiZTwWFhYWFhYWFhYWWxTXlNl3uDoJAobsDYkXwKw7lArX94FsFCcLY2QxcKG8okPWPOL/XJXKdIT13ShqofsN7fiy7z2vuR7qlAtEF4UOlNkPAad9HeUYuY5uyciFYfT5uQs0KNOplWTV2KjIKm/nDmFWykUJM505cR4AkMtkcOyoMPkHH/knAEBPUn7a06eFtd+/V1ieYklW8IP9fThxkqFhrlJzXVles5xrpcrPycrEnBhqNfnt8pvCJumK1OLbC98Kk99Ekwk3jMI3+Q0HrmlKkdsp23HaX823XThRB+/Q8d41Wyt70RlTaPnMBP80SkiGH+0M/8Xft/hORbmYBwCsrUtfOTEh7N38hfPYvl1kisoibyOLvbwkMoHVokQBxrcJ03d+ZgaFguzv5pv2AQBcSL93+KT0sz3sK3szIhH46Ac/gK6sfPZPX/g8AOCB+94IAEi48ry//aGHZB9HXwQAHHzhBaSyEm0YzEmEdH1NIrA93dLX757aBgDYOTEFAHjssccwOCj/Q0za18q6XMfgyCAAIJkRNvKlYyr9EUnO6LZBpFIS+TjKc9DI7/RpiQrccdsr5btHTwIAKuUi7nvD6wAAm3mRcSytCBOrbWdsTO5nPCH3UZhfyikYIQj4Ph6X4+uYoixxLOaiTrlQluefiOs4bnGlcexFkfmqLCXLZzfTkzQyFmWeN6vtrLZGenSMiTmuif7r3EGh39HXyUlpi+VyGe985zsBAB/96EfbttFnZHFRYgy7du0CIM+KMu163no8ncO0RgH0HHt6pI0ee0mu+eAzEmHT5/bAgQNynKkpABKZAoS1V9a/wf0neJ+GKY0zcyXek/X1dfNMZ9i2j7Ed6nXpPRgclPY6OztrvqPRjdaod+urMv2u614kfdf7dznYkc7CwsLCwsLCwsJii+KaMvsJHs0JO3VI8WYigtf+P5OwaxJAdG9hC3vZmbzb/l51/k7U1Pk3ucqXf++6rSxnJzOo59g8p+ZJaLRB3rpGo6+MPldoHYd3ESHbJStAvy6rxmyvrGbXl4U5cZiYOzctbMyHvvQF3HnzDQCAWkkY923bRbc5OyNs1mZevqvsSalcMPdcV5r5DWFu9L7qCldX0w1EZgW9tsmVaDIBi+8MXC6x3PyfYSfHbSbJm1ah7VHfd7wqbyDPe3sbBp8z0zqc9u8AoWkfZpOLTlVZe90uMK9O5zbmgto/17Z26Xx9y3t8JyNHZnwwKSxeMi4/8o7JCfg0LxgaFq28T52ty4est1dY5m7mHzUadezZLWzii0dEx18pS3+Xz68AAOYr0s/e88rb5f3cLH7o7e8wxwSAbEr06ovnJQI7NSWfP/f1J+T97j246bbbAADrBWHfr9st/fbv/vbvAQDGhyRCsXDuOADgjlsPYI6JkpnuPgDAmTOi+9ek2iyjDbfe+goAQKkg28+en0atLttsGxUGMctr1gTBtVVhU2tlub6f/un/gJkZ2f/H//6zcn07ZLzQPDv9f5ZM5vDwMBYXZX+aD9bTJ/e+v1/YTB3PNQ+gUCjCoyZccxBU721x5eHXmGiu85FGU/OuzL4yxZ2Js6rlN0xy0Ox3Oxl+fY04cVN9PgB85CMfAQCsrEibUqZd8wc0h/D4cXn2h4eHzZjSqdFXRl8ZdjNf9DyTm6PRC70eZdOffPJJAM1owY4dkpS/d+9e8/fJkxLpWmOysj6jer36rEZRhIQnn5WKxbZt9L5qbouqIpLJpNlG749GAToTdfV9IpEw31HoNpfDNZ3sB0we1Tm6jrkOHCPxgc8HSEMT/NFMeKgl6S/qSLRzOiU5hOu+/CTnG5x1ywH01Dr2d9HnkfkwarnK1vdux0LBJA06QFiTBzjBr64tyAOWojvPsxwsnn1GHtK9U+M4f0YeoJv27wYAbG5IaDe/Kd/tZgJvPt8Mm3Z3S+M9dUIa09AIs9W5qKnU5OFc58S+UY9MQkq+KA97T18vLL79cKmJfednl5386+dhgKhjEq6JtDphNsm2LVPuzgTcMOr4TkcGrwvHHMcJO2finQvsjgmAExpJ3MXbKjoWEiEQOe0LEJuy+52NTJpuHBwmSpys+lEDlYrIEVzKXsIumZTG+HxuUgbjcrGbzSQwMSZ94ZmTRwEAo5TIbBuR7951h0ykP/KBvwAAvOV7H0QyLuewsSL7G+6Viey5MzLhPX7sBQDArbeIMUKxWsHjX/kiAGAvzRKefFQSjfszcq5njz8HAJiijCcd68MmJ/uJrCxS9u7dI+/j0sevrspkSd2HnEjaTDzuQecHad4vj2SWmj68/t7X8HM6e3z0wzg/Ow8AqNSF6Dl79hz3JxMSnfTpZGNjY8NMhjQZsplsKROgGq/B5bgej8eQjFPyw4TihTWZBFpcefTQcUcnqXkmzpZKJfOZTopTfK51EptkEnzrhLvTuEMn974SpJxGjYyMAABmZmeNM1MPk1KzXEyM8ZmZn5fnbpjfKRaLqHH+oRNdPa4+g53SFvg+0mmZ5Ncq8lzp4kIn37qoSMblek+flPZ68vhJQ27qtro47d0h56wLBD1+d7YbfWz3m8VS231UWZReV7GwyZvVZQjVJBlx3+c4F8l+HTVJUZchzwG8f57MzdJZFhYWFhYWFhYWFlsU15TZj2kOrqtJBxruD83KSKUyDpP7krF2SycjyXGakhzHJMOq/KCdOYzp/53wm3XcbIb6w+ji/7nN/13qu+38ol4j33YSl07n1iHKDBFnGGJ16hKi+sQnPgUAaNDjedugMDvnTr+EyTFJLjt+VNij66Yk/HT7rTcBaIaMlpZkVX5+ft6sVo0F57lpOR7vn1rVafgu8CMU6SHrM3SnK22Lby+8HIvfKZFrfk4WoW3bzh3Li7EAc7RWRjNZPow6GpHKgkzgS+U9jnnrhdIVNZsHE3GdptSnDY4m6kYt7Z27NeeqfUe7BMiN3EtIiiy+k+GwlkJAtrEnJ2zeytoyRoaEudtg0m1fj/SbQUOingky/ioz7e/N4RzlJwXKdtJJYdpWloQtf+hBSb598HvEZvNLn/+cYRuXFiUUX6GP/+qavL/1dpH8NMrS7zoOMEwf+i997jMAmhHsxx9+FADwi+/+OQDA178m0dwnHpvB7a+TY/pxGR+WF+U6nn7xWQBAvSb34oZ9+3lzmMi7vAgvJk/+0KAwk9dTrnTPPfcAAEpF6d//7m8+DgBIJdNG7qSs7HOHRdqkrL1afBaKMk6VSiUz3mg/ofIK7XPiZFFV6jDfdLGUAAAgAElEQVQ4MGxkFT0cd2Ixm6B7tVAvye+h9YsSnG8hk0FEdUWDdseb/F02GI3JMeG1NWnUZVQm1sE2dzLvBf7eN954o7GK3blzJ4CmROYZJtBqYq4y4lEUmf1oW+u0pNRok/7fcTycPy9tVucx+mx2SmRUtaCRjTAMzX70O7rteUrzVHKkn09MTJh2Uq9LX7TK+6ay6M5zXllZMcfROZeRP3Uk3+r7byTZuRQss29hYWFhYWFhYWGxRXFNmX1d83kdmt4gDA0b6PGfaWqYUnR2zBe0WELTmjNsYRMFnXZ8+v8mK6gJvuE3+95zLrPXZu5B5/+DqPnOsKdee0Ku0eo363CZ/w9vE33o0qLYPh1+9msAgOIGK9zSAq2eEXZkcnQQJ18UtmXvTlkNv/DCQQBNrdnZ6WkAwK/92q8BAB599DGUqclfXJJKcpp4ozq1bJcwYB5XnfFEDHWyv91ceao20+LbC5di7zs/u5jZZ3tRLX0UovlUU+PewewbXT6aLE9Ts6/J9R3hAW1kGkkIXQBaIZcvhnFXBr/F1lZOsokOdt4czgQWtJtTnb7botHX/V1O72/xnYBySRjD4VGxxJth0as9e3fh8PPSN+7ZI9r2+fPyP9+X33xsVOwttfLt6PAIknEZf+oF6XNDX/rEd7xdCmRp37xzt+RIDR4cRSEvfWF3j7DmeVbQLFflGX7kUenH1SL6+pv3Y2qXRGBVD61FD284IJafqqV/7atfJecDDx/5/JflnOISvdh9nWx7yy1S4TbNglVnz57ldfFe7J7C2WnJ7UrGJaJQLAoz+Z/+4y8AAMbHJPl2bVnGmHSqGzpya77Xvn1yvG3b5L4pI6tRget370GJTP7hw4cBALluuSdazVeTOrtYGbJeLSPQqtZk9Hu2CUNqceURc6W/DakND5igGwah6RLjrPzqxbWgqZmlAGiZd7UkwRpGXU0ZVMvP1yKjP4cOPW906i+8IJaYasc6PCwqhaUliaq1FotqJtly7tih1Y/H25OKPS/E0IDMpzoZ/ESsPQpQpWpB50yxWAwz05Kfovad+h1TQTfQyIW85jc2TWSgj8noWvxOcwW0XSp7v3fvXhN1KDLyoW3q5cbsfy67b5l9CwsLCwsLCwsLiy2Ka8rsn6NGKxYny82CG35QN+5NKboE9PYIo5HrkRWPrjKN3aXTdFPoRNOVRzXBF7N2naucy71vXT3pqsqo0lpKF7fCcxyEUQdl31GcqKkr1vcahQiwsSy2Zc8++RgA4NDTotcssyDWTTcIszI0IGzJ8uICbtwvlm3gSn2gT7Rfo2Py+dvf/gMAgGG6Srz61XfjxWPiNKGFsmLMutdyzrqiVls2eK7RoekqvFItweLbD/8cZr/dRarpShIhuqjtqOtOqKyO2nWaKFdknummLr5jH01bHvPenIN56XCz4j7CS1jluh3dQLOontrdkokydreeKbAVXRSvs/hORG+vjBMOn9eYJ8/J7Mw5FPPSb05Piw2xspqVijB4vTmxsLzjtlsBAEcOH0aCw83NN0nBnYe/IoWyervvBwB84QvimnPDfrHOXFzdQBfZvjwZ7mRGzmljRjTDGTqixcloHjr8IroYBSiU5FzUek/16qfOiOVfLiX6/JdOTiOi9fLYmDDrabKYRw6Jc88tt8h1XM+og1+TPrqrK4MHv+cB+d/e6wAAc+fFNrOP9qMvHRGWNcUcsKABbGxSVz9Mpp36bmUqNbo7NCj/X1tbw9lz7YxomRpxjRoru3nnnXea72zfPgWgqYfuLNBkceWgv5mJ0FJ5kE6mzH3Xz3QOpMx+I5A5gLLqtVrV6NHREfHtRLVaN//XAlL6LOgYoBp91a/rM9NoNIx2Xl1wlKXvzAlRuK5rPlundl631WdTowXK6Ov/4/G40d9r5EA1+nrt+h2NJERRhNqiKCXC03JdKeZejo5Km7rhBrFJ17Z++vRpc456Ty4Hva+e512Uc/CNYJl9CwsLCwsLCwsLiy2Ka8rs18uyYqvRdLWVVQtE6I5aiStDLtCY5I0+FuVQ+GGEgCy261JT3qEtU+1u5OMb4nIr0ZjrNXVnHatG8x09XovHqzIzeeo442RDxgdllTczp/73LFfNleE/fPoT2FgRJn11RVaIOWrzR/qFyTny3NMAmp6scc9Fb05YpIUL4uGquQGL3NeRI1LmvZtOFPW6j0Fq43TFW+xgXwaor0yyxHqI5mpbi1gMDbX/LhZXDpdz1Imi6GV095d+H4aheT470dTfa20LrS3RqtlvfgI026ey5UqvJ1y3RWOpOkpTTQ+AFC2Sc5Lt4l4KDtlWv8G+gdvGEmT3PPVVpusKjx+LxVC/KPpARorF+0wkwdS4cJuuQvQgb7r7tKO1X+jsI75RkTKLa4djL4kLWTotfeUg+6VcXw4OtgMAbrxJnMmee1Y0/P3bpIjP1ITo1E8ck3oj+/fswmNffRgAsLkkzPc+utb0UFN/843Czh09JezdeqmEVDLLz44BAMbZv2Z75FyOvCDneOfdrwQABNUG6oE8o0trMk688g5h5adPyz5OnhaG/L7XfjcA4ECqC/OVF+Wi2X7WV1mUiBr35w6Km8mdt98hm7Hdnzx2HHt3S45Agfr7/ddLHsP73vdJADDa64htJ2gAt2nhr5K02wK1zQefEz2+Fh5SpjGVSmF4UCLIOrb0cHzasV30y1pzR73GY56DxQUZu+o12X+lbLnIqwWtk6Pacx3Xc7kcYgxrqfZcXwP2d53FzhzHMexy0OFzr7UtTFEojgXdXTmU+Bxl6PlfZaGvgUGZd5jIjklsdJFKy3ypq1vmMTrWeNTSK9OvboF+UEMqIW02Rb/9GJ2gdH5YojuW/n+YOS/1ev2iPl/nPXrcOLfV8wnDEE5D8wbk/APWp1ijYkPfJxg5GRkcMBED3UZ/F81rSDCHSAeuTDJhzqXe4VB0OdjWZGFhYWFhYWFhYbFFcU2ZfZiqYCpUZ0lj14WuWHzqx9e5wilTb7mQEN2jeq8ODnajXJG1iuqmjLaM+62UZOWjmc5Ak/EOjOOIvA+NNDhqex/WGuZvU4iXKzIvcjq+K6+NEKjWJYoxMiQr5kZZ/nn2nLD1E9REVlll7c/++38DANTKRQQNWfF2Z2R1t3NcrvkvP/xBAMA73yGOEEePCcMzOTZmtPMD/eKtrDp7rR43Q6eJlbNS3XH79u3Yu1f0/I8//jiAph5NdXHKwO7YIczYkSNHjHOPevMvLi7C4urgW2H2FZ2VBC+l3XcuyjnpsIpqqQRtcmU632vdC37F9+vNUuUqzQ/r/AadEwJhNjzuy4vqCBuMnpGy15QXP2j3U/bJ7Efq3Q8PYdSp7e1ol/y/G6kbT9R0HNJaHZeJ2rXeq2+6ArHFNcceMu+NQMaPalX6/pmz01hdFW1sd0b6tS7qfbdRg16ryLaT1MA/8pWHsW+v7O/CnGjm73/j6wAA//SlzwEAbrhRKug2WO01mUrD53Onr0dPiFe/+tFP7hQNfeAwihW4ePawaOSLJdE8f+zjwrBff51EG1TD+8xzEpndve8A7n3NqwEAX3vmeQBAvSbnn07LONfHyuhaGVijvuXCGrZvl768UBBm//3vfz+/K/dmmS48G+us5TI6iYDjnLY91errdSnDqNrnWq12kR+4MpXPPiu1AJSxPH1a7tHk5KT5O0MLvu5u2Z/FlcfINuZkBEHb56VKGVFZfrs62WvTB3rt3LDOoV5uPGpGkzlnogrD8zzzTOgzYlh6zRlgH6zbxeNxw17rtnr++p1W7bz+P5GSz+JRu4NOiPaxS3MRqtTsV2pVhKw5kKbuXnMFdFu/3jDbAuLWo9teuCBzVs0NUKjjjp5rb2+vaTua+6DREz2efq7fmZmZMfNavReDjKZdDpbZt7CwsLCwsLCwsNiiuKbMfq0qKyZd7elqLB6Pw1W2XBl1zbLm6wZXOsrKpBIJpHTFRt27kXhxH0lPWHX1U47gIgo72Tm+Nm1A2t7nNwrNQqDGuNtpe6+Mh3rVNgLfrMC+8rQwGQO94vjgUl98lFUI15aEGc+ThakUNuCzYu6JJfFyvvce0Ws26lXeA2FFlM88dvwlDA/Lqk5XkbuvF7eFmRlxNtCM91/6pV8GADz6+GN4/nlhhtQvWVenythMTU0BaK5Ex7YNYZn6UJCt1SiExZVHp8b+m3HYebnvfmNm+mJmv9ONR5l8w/boP9jo/CBskVjyu2RS1EXEJfsaY2OPuaHJq/FMMoB2TV7rLkz7Dfm53wiNO0jY4XWlTL5johItHsXm/DuiGpdBFEVWs/9tDP09e3PS/7m98los5dHbI+NAnKxgP/viLjLI8Zj01TVH+r8H7rsP+U2JBjz2ZdHxG52tVn9n/Ycnv/4UACCV7sMC+/JyRbZ1yGKqm43xkWJ+SnfPAI6eFEe03bunAADLZM1peoZylW0lJc/4hQuLGN3Z13YuLx6V6MAg3XA87v/c2WkAzTyGiYmJi/qFhWU5nuruk9RE7xkWbX1//xDOz8s4VOH4pizjwIDkIijTry4qrU4h6jE+Pycs557dMi4dPSrXrZGG5aVFxKnv7udvt7hwARZXB9098gxpfp7RuocB/EDnMe3Piuuqt307My5Nj/O3DoeYTkefwGnNa5T2oVEeZelbK9gCTS26VscFmmx2syJzvO34rfvS/3UqNDrVHA6v25xXlDW5luqoE5DJn+OzmeS+u/nMBvWGcZMaGZGonOat6HXpfEuPs7m5aeaL112n8zbJFdLfRed1qtgYGxszbj7K/qs3/+VwjWU88gM0bSebyXb6I3la9pgdpUpmupnEsTgnSTylzTx27JwCIGEQACgzgUgfgBRvwumTZ3g89589QHued1mrQn0Y9cHTH9MPmokdNSYln7ggD0eCCSpxPvR+QwaYASbOnlw4h128rrFR6Uy/+sjDAIDbb5WiKSq70YlYOp3GgZtEVvPoo1Jm/d577wUAzC/Iw1Hng3zqrNwLx3Fwgeek1zMwJAuGHMO+zzzzdQBAsSide3d3N0oswpLfZKJMVhNHLK40OkOsl5rkd6Lz2bzc9y+Nzsn+xdKVUAtvGZkL37vNY8Sdjok0t3EDaZ+uTvq5j1joI/DbpXjghMVxJPTqc3KvSf2+lmGPIrhe0pxv27nqpyYZl+eB0Jx3SGmRg87rapc6tX5m8e0HlRMOQ/qwvj6ZzCSTSSNRyXPSnYrJBGduTfoyLfTzhte9Xj4/dxaFTZG5/NA73wEAePqpJwEAe/aIvOZTn/wEAODA/usBAEdPnEOSSYKTEyKROPiUWGGWy3K8+94otp06y4jHUtizW6SUJ07KhP3MWbGnzpLImp+VQf+eu6Wo1mSuFw3KZh584E0AgNIGZQEJkQIMDcnxNyjLbLAg2L9+y1uxsSkT87//9N8DALqyMu44bF9TUyJfWl2TPv/C/LJJXNRtdAKv0Pc6yenr6zMTfx2bNQF0fl7G7+uv533jpN8JIzPp00mMTnQsrjzWC/L76hij/ZzrunCZoKsyTN1GSc+g0xDBccz8TSU3OpHtTNCtU6YdRZHZr0lC7ZD16HxK37faaCr0f3o8M49UKVDMQywp+zXJw52vvK4YJ+4pymMSyaQZC+f43OqYNTEp/UAPLchVJr2xvo6IN07l5Wq5qfPEWW6r5zg+Pm4m82qJqn2WLlTUDl1lffl83rS7TuvSy8HKeCwsLCwsLCwsLCy2KK4ts89wSecqzA8DNDpW8SYcxNduhl6Lm7IiXV5oJoau59ROUi39ZMWWyQgj4YWqK/AM+dcZkr+c9Wa9Xjcr0M7XTmbfvAZ1LLKcuoaQU7zmv/ubjwEATh4VG7Z775Fkq3tfLVKde3/sx3B2WhKV1lflGsfHJezzvj/4fQDAfW+SZLEeRgP+3b/7Kbz/j/8IAPC2t70NAJBjmE5DcT/+rp8AAGxuyP2LANSZMP3ggw8CAP7+438HAHg1WaRHHpay7Dt3TgEABgb6EI/Lb6ir1p07d1zyvln8y9HJ7Cu+GYb55ba5ODG3/TtOy3uVxlyUxNv8Fl/UMrcBJ9IEdjJAZPKdSNu4tBNX21HQQL2gxYQY/o0Ls+HFpQ27fE2QxXdChnT9EA4lPyrtiRyyVYwcmsR6c+6hYfKbsj0NL8v75j1qXunl+giL//UY7JcoqI4byiwvLl4wIfLNTWHNYkzYLtP6j48JPvLhvwQA7N2900hgYhBpTH5d+nMvEla9zAhnpSj94FB/FivzwppvrMj/dk4J83b4WTFSKNFsYs8+MVHYLBUxOyfMfQ/764ceeggAMHP2BIBmgThlBw899yx6B7a1fUcv4PChgwCAiXHpkzdo+9zfL+z6wsICFikNVRZeGfaVVZEaBGxXRUYjwrqLZEba10C/sI+ZLmmLKilQZnZjTfaxublp+gtNus0x2baXxbuefvKJtuOfn53F/Jz0AXv2iB1oOiWMrMWVxxqj9aZQaAszr+oKlSdrNFf7Qk14VTiOYxjoVILSar5vFuSSbXPd8gzlS2VjZa4FGrWopxZ11PmiWqnXg6DJ5HP/JoLQUVjKYaefdBImMlXjfCeiwkQjzirNafB6Pcq+YykXRUrWspQHJmgBPTsvsjTtU+qcZ01unzJJ+yGlf2qHrvNFTaTV8eT87Aw21iWaoNLpFOd22o9NjItN8MmTYhgwPj6OM6clCqgyHt32crDMvoWFhYWFhYWFhcUWxTVl9qu0wlQmwE3oyj0ydkgKXdwpu7nBJIcuWhRFmYzZX63cLG8MNFd7GxXVBstxQjhwHE1rbbeE0oJCaCn0BQBR5JjiP1r4q/O9ruTqdWX2a6ixFLtPduLnf+4/AAC2T0jyqybzfu4fPwMAuOWAaDc/8YnnUS6KXhRkKOtMzP30p0RnqeWW1zflnhx6/ghe9wbRm3718ccANDWSWh9pdXWV908+Hx0dw8CAsFYvvSR60VtukZyACwuyat1Gey693pPHj+JP/uSPAQC//Tu/CQB4+MtfBAC85/+CxRXG5Zj9bwadrL3jON8wmtVktbWNBC1EvtuxbYd2UrX7CJsJsXz4jEZfs3AjaS8BtcR+I4/KpugYXUetNYXZd5OMjHWxwFtWWBGXDEsQRQDzUYzuvuNcNZkSaJ6jiVNEHdteJupxKetNy/R/+8AU4OHAkc3JONHV1WW0wNpfFwqicS/mhQlbXhBG+aYbDwAADh18Dp/5zMcBAD/+b98OADh3hhaZeYkg7L1ebDRPzYkV8Vp+HXFXTSSosz0pz3Q6Jc/qDmpsa7QFzXVnkaDO/zzzp4oFSbILanKOw0P9AIB/+NSn5f3wMM5OCyuf35Tzn1INPbuLoUFhAU+fFZ1vjEmGf/2Rj2JoVNrPnj0y3qTTaiko577IhN2enLS3TK4bSysydvhsPytr8r6z6NLOnTtlH4uLJmdCx5YqC16qXvn1r5fx6mtf+xoA4PbbbsMLLDqmY1dn8SaLK4cYk7aNhaUWzAp8+NoHop31Vza9m3Ow1rwwExmItc/BjAUm22U3mf3QcU1ysKLTrvWifbQw+zrXa03abb0ek4PgefA7cgw85iQkuf8gJQ1H1QqtY4Dq/fWZVIvMDeY85Kgq0XPMZDKYm5F2V2XhspGRkbbjq8W5nms2mzVj/XPPSZ7PJHMC9FW1/Dr3O3XqlNH1awKw5sdcDpbZt7CwsLCwsLCwsNiiuKbMfpbaKV0FRoGu2HyzmurUejW44ikXhA3RFVYqmUJVGcJAXX7UvpMsD5cySVNq2EUU6aqNDGLHe/2SknjVav2ibfV4yuSX6bijrg7VahG9/bLim2amdK5Lzvv0SbFy62XGd09OmJX3vvdXAAD/5dd/DfFRLTUuLMuBG/e33bf77xdXh+Pc14svvYQ775IS7P/Pf/51AMCv/IrsL8lS0YvUV67GdBXYj//4i/8JQDPT+/3/7x8AAH7iXT8GAHjorW8FAPzu7wmLv3NqO84xn+D0KTn2b//Wb8Di6uBSjjrfLLQ9tb52fnaxrv9i5t8xn12sYQcATzlyrSgXBmjK4dVCTdqp61Crz0hRjYXgGqUVOA1hO4KGMJrlBo/nSTtS1jIeI+NPdiYWxo1DT9Ous6nNlzMns++ohj80tmuI4vyGtvuLXXgsvv1his6k5PdU++Cgrx8Nlq9vJOU5ODt7jt+RZ60vJyz0c88IqzY2MoDt48KWnzwm/dzu3WIROTAgz9/0WdHOvvGBtwAQO80v/KNEOUtxiTQ3RiQKkGEfPD8rY8Edr5T8rMAFIrLX44yiJhkFCOpyboP9Mm7MaR6X5+B73/p98vcGxx06B0UsgjjL/nxsVJw7evslOtDd040Xjwl7Xs4Lizk7I1Hc6/aIdr7uy3O/siLjRDxegevJPZ2jfWaFkeaRQWH/tZ/KMkrQ19eHXrqLbCcz+dIRsXm+6667AACLCxIRmaLl59raitHzq83psi3YeNWg+ni1UTOFEF23aZupRa0YRTVOO9Tlm/EpCJvsf6x9/macfMiuK3sONOczuh+N5OhxNdemdW7Yyf6bueTL2EprLqfmKJqc0Xj7qxbdMsdLJkx+is6RNHp17+slb1Idcb74RWn7hUIBPf3Sn2yju+HGukTrlMkfGhpqO6/l5WUT5VD9vbqL6T3Q42iezK5du4yzVT/bt3XjsbCwsLCwsLCwsPjfFNeU2d+xQ9gRXcVsbAgjUS02V3vxeDs7py4Zca5E89RBOaWi0f6rIaz+T5FIyP+dNDWb8MwqUl87nXU6vfRjMReOFlJRATwZwsBnWeUyvec3ZAVXKpXwiY8/AqC58iwW5Jpf8+o3AACeP/w0AGBsXJiPN7z+XgDA4soyXmDBrf0HhG358Ic/DAAY3z7Zdn0xspujI5OYpY7zN3/jdwAAff2yetTiDGGvXEOhICvDQr6C97znPQCa0RJlx/70T/8UQNPZ55d+SaIER44cxvt+/w/lf98vWlYtIGNx5eFEl9OsNtfo0WUZaOeiV0/N8A0bz3dRe1tzW1h88w1TiKQzKkAWKGq2H0cjbXTfcUJp3zG2G4/vG1VhL2ulDfQkZb9lOiNEzMPxweJtdOeJ0xdcy677fhxRXK+H7ZyRPcPeQ10fjOG+cehR1r+Zp9B2hwzzFbVcqynG0sGVNN81IzLORcGTbz1aY3F57NktGvo62cGebnlO1tZWsLku44wydsqodWfpv5+X/6e7hEVbXF7Cj//kuwAAv/97/1W2KUvf/q6f+GEAwHBZmLb1Ffmu67roJrP94JveDAB44jEpuHXmrOjUz9NtZmVVGLlkNocdkxKBOHpS3HeWF2UsObBfrqdBltshu37jza9ANi1R4Wxa2POnnxEXnm0jotU/TgeckdF9AIBjJ8S1480Pfi/OnJP/hXxaM4w4nzwt9VdSaXk/Nib7agQBioyqKyPa5WR4/2RbHeNOHJNr2HXdTlPDpZe/w5133innyIJCK/TSVzb/7NmzRg+ttWD27JECQxZXHqUSnaioWoh70nd6cc+oKwzDzZwPzf3orOXitFuWyYt642uklNtq4ScvEb+I2de5mEYQOj37gyAw7P/l8qcuiiiEoXEC8tmW6izuGjASpt9R5t0w/05z27FR0d2nOOfS2keTY9J+++iec98995j9ffkLnwcA5Fhwq0zlR5n71GFiYGDAnL/eiwVGvtRhR730W6PyGiHozDW4HCyzb2FhYWFhYWFhYbFFcU2Z/c2irOq0ClhPvzAUx48fx513iOb8BL1Dy6wSqO47Ra5EE3S3GR0fw6FDwoD30IN/1y6p/nfkyBEAwNSUaKYWFoXNHh/fbrKaK4wuuGQIe/okk3mWek5dNQV+HY2anAvItIbULGZSslZKDUkEIcbFZrlQQjol+ytWZLU4PCIsRakqGw1vmwIAnGeF21feJczH8y8eQiwpq8d51hLYuVt8h8u8B6mUMCsri3I/z59bwgI1Xq+6Vzzy1WQkCIVhofkP6jWNlCSNnl/v/VcfESefnTvlXB/+irz/p698CQBw4cIc3vQmqdr49LNS+fDg88LmvOouqTRpceXgBu1Rk6bO3G2WNyQ6WWaPLEzbal8pjo5cALcjmqU5L9VaAzFGx1wyPzUtZa7aSVZI9Buyz5TnIsFeJUWP8CS7mcqasHlVOo6EFWExe+Ih4jy3lCfHKzWE0RjpZ/VOvj//olR1Ht8pzim9/RPI0xe54ch3w2SWJ5Dm56wNQc1mAB8MJCBDZsvxWaOD56F3KALdHhyvWXOAbL/eY1O6Xcu7c9+e22RTmk5BFlcDHu/sDLW1m6uS7xS5DhbnpQ9U1mxoQPpm40vtsPI5I7f5yiY+9dnPAgDuvkciscWSRD0/9DFh63YxSp3i83/bK27Gnbd/F/fLyrWQceM1r7sdAPC77xMns1fn7ua5RcbNoysjr/v3SyX0Y0dF494/IOz5ZplOP7lBFIsyDnRnNH9NntE33PcaOX5DznVhUdjA7l6JWCyvLmB4TJj146wqHzJnpc5K7hOTMjYbPfHSHLq65NyGe2Q8nZmWSMWFqmj4VTPcnZLxfOXCkmHpFzbk3rOpmOe/j7+BapNDRKYgxv4bJCLRzKOzuNLoywjbXGHeVH/foHnvsi5RF/MKi0WJwGZSqtWX/q5Zv2ITPWnOgeheU6nIM5rl55Wazl04jtRqcELWUKKyQBl1VX7UmAvpq9MWXNOGdVuno3aT1lhJcBByYh48T546j+OAOiFqnSK9vgaZfqMu8RtmTtfgNgtUo4zQ+aZIPb7LvuOLn/lHxDsqAX/vWyT38eDTEoF78ZjU3dA2Ui4XkaRKRSMX2xgxKDHCXWu016GKgtA4XqnOX/X8l4Nl9i0sLCwsLCwsLCy2KK4ps3/89DQAwGU298yM6Mx37tqFE9Pyv3FmOz/3nLD2A6xcVqE3csDqh8VaAzF190nKyiaWkZXo9t2idR8bF/Yl1ydsRX//IJ577jAAwInJOfyaeLUAACAASURBVCQywv75ZOuyveKgsMmVaW+uG/OshlvMC1s0SJ3hiy8Iu726KBrMN9wr1XBvvuVO/NmH/z0A4Iabhe1JZ+XcnjskzOQUGZTdO0T7Obcs7gfv+b9/HZ/5B/HTLxf1muX+3f0aYe2fPywrwxCy8s3lcnjnj0iF3BdPyf/2HRB2JJmS+6cVE/ftk/v7Yz/+b1EtyPfPnBGWZ3RU7teTT8kKVHV399xzDwDgjld+F+68U1iq//an/13O5Xn5nSyuPDqZLa1MG7mh8YdvKhdVl97+Xt+6UXPb1gq5/KPtOMqSoNbMGagyuqWuNcqCppPclmxPqbSJRFq2KVWFBWkE8pxVWIW0n/8PeX0xp46Qzj2zM6Ip3quVPRdXeP5ynAOTEnWaXxDWcsfkJBp0FnF43uVA9lUhcVtVxwnSi14ybZjGRkUifa6h8snsq1ZfKzM6Doxmv0OHaqp9x1SnShY/8E2tAeMM5Fp+5WpAWXqtjqnsYDqdNsyhbuMHwpJ5DMHEE/L7aTS3pzdr2D5Pa8FU5TubG/L5ydPSn558SfrbbcPjeGlDWOqxcdnPdXsl0qz+5O/8oR8AAJw6I9HQruwoKlX155bx4cRRef537pLnPwhlHErmGRXftg0TQzJmzE5LFGPfPunrv8yK5xsb4oM/S93vnd8l40aurwfDY5L3dfS49PmTjFDkX5SxTF1HRkbkGpYW5zA4IAxif4+cY8KV76ysSKSuK6OMr1yn7/uYm5P7k0jGzGcAUGTl1r4BiQaohn9gYKDpSMJqu1pp1OLKI8Z+KM3ofqR6eS9hqphrXlMmmWp7Hyejr/1eq9e9iXJqtFir43LOl1CHnVjc/N5rdJNKJuVcurpkPpdjm2itiq1jk74avb/meml+JSPanueYdp+g3l7rO9U1H4b9tTL9Ce57bW0N20bH2o5ToeIkxnOq8vMadfPlchkur0ur6uqcc2FZmPcdO2QOpv3SsWPHjJKltZJx633U64nFm3kTrQ5KQLPK9uVwTSf7XYMSmriwIR3YJlUKh46fNRc5sywh0PV1GYTn1znh7RhYV/J1LK/JQxKqnIV2YZqcceyMdDg5hkh37PJwnpPqJMNLYNJEkTe7h5ZhtRqTCxMB8lU59uKKdFTaEd9zn/xoHkNHDSZe/OGf/A8szsnEJt0n51DYlHO89U6RzDz7rEz6X2BiVn5VOvlf+bXfwE//n1KhqliS6+thEpU2qp37bwMAHD4scqXhwSHccodMwquUJRWZOJtISad6190yIGTZcC8sFlBmOfWhUbmOX36v2GiepKXn/IJcwx133AEAqNRrOHOOoSJPGuIbH5Dy7hZXHn6oFrKaScsJaAhElNo0p+mcRJoZfXvCqYuomcSkIhW+mCJY2rFwH/UwgMtZcLUuHaaGbkE7TdOBsmBW72ASUUWe29ExeW6ffUKe9V3bZCE9d06e+bFhXYieRjohbeim/dJhvfDcVwEA+/aIXCfO5/rYoccBAFN7bgUAfPETf4MD33UfACDFBEujcKK0SGuqeAztumEMIb0865EMLFpUrzNRF0Fr4r56imq5dYZ9OTgkPP4GKpMK64i4CFKZiZXxXB3UjCxTfgNNWkulUkYqo9IUTZJW+YlO7LV/LZVCLDOBVCcVGipXCWqK7eCmW2RQfvjRr+L8nCxAB0ckxP/Q94kt50Ze+kyXBFO9Ic9aV1cX8pS06fF00B8clAKH+YIsdvt7ZJ/PPH0QJzNn2q79Td8jUqPVgoydG5tyPesb8r5QVNvqHC4srPBbcg+mz8q4s2unLAI0gTK/KQuGGw/sh08jCj+Q8aJ3gBaFHAMCLuaHuEAolcpwkzJRHx6S9vxZyqLe/GZJXq5zUaYmHdft2mOSel966Zic0y4r47la6JxUVtk2UqnURRNNTVzX9hM22m3KY/FkkwTSwqMkTfS9jkfFqkzKM5kMujNZ7lcNHCi9YV+fJxlZ4HPd19cHn3O7akdBroCLDE+lQJRs+vWyaddqvdm8ZtmHyjFjCbXeVHLIxfxSez+g8iSVOFV8ylnJsjmJtCF0HI4P0+dE9ra+Lm1qJ/sSvRY4DhJM7m9d2Pz/7L1XlOT3eR14K+fqquocp7snDyYjDECAIIYkIJAUKVKkKFGyzaTgXUuy7Idd+xxLOse7e3b3nJWDZK8kSlqJpixRYpBIgQkQiZxnAMxg8vR0zqFiV077cO9Xw8Ga9DnrxjzA/++lu6ur/ql+8X73uxcAGnYfdn+1m/NTray1rMait5p3vTUcmMkJJ5xwwgknnHDCCSfeoXFbkf2Nbe5S1tdZ2GMo+vSNaUyOM+V5bWEWADAyQqQhm+XuxQo/zDirUm4gGCNSWNeupygjHr+fu6QNIRxzS0Qzik038kr3+N2WeiLCkd+WxJ9Xu1cdc+nyFPbt4bWFzBhriUjN+TSP299DhCi9ybTpldlFDO6jHfnsBRZadU+M81pV8Lf30FEAwLaKuf73f/8XAIDUwB782V99g9egYo2YrN/X11f13JhWTev81xfX8NiTREJtZ2gyi6WyCks2iMpkZH3uAtDUTn1Q6R8rXrYiEUu5vn6BRdNbW1sdgxaThtqzj8XD+Hk4scNRa1sRktmXG3rv6qQrjc7TCb3eaN2aznTD1UlXejomVIZA2zEsJao0ZKOKkFtpV9Fs/ErxuppEYwJCY9p1tqt8egvJMN8zdUlGJEPs5+srRPRHx9hvMzYORD3oCrHdLlxjturYPprtLM2z7Xklq3lyHw3mZhZnAQDH9+xHeom/xwd4LfEuZq984LVHBS65DW2qVpCv8B4DUaWxZRzTMRFrm0SvaY42aBiGHyq27dB1NHZ0ZDr1Pper0x/tuK2mI735doQhX0ePalzV2LW+vt7J9Bo6b8W2s6KOGsJvtJF4PNr53ixDYCnzYpHjqSHSQRWu+yI+7FVx7YsvcSz+87/86i3XdmA//29Fd9/6zncQi5vBDq/RKDk3JJ9pyP/BQ5TiLJYr8Lkrt1x/WcXlK6KTbnfELZgNgJDSQnG7g0guL7PvHTzIzFm3Cg4tA53Ss5ienoLbo+xeMa3nxOfVlWTG5LKoR5Ua57KN9U2kepkB2djkZz//i5/hPX/r23yvKIKHDvL7mpmZQy7H72XXGOdbv+R2ndj5sAyYodzbP0QjMSTcsmWGHNvf1icsiez3+zvHs3grzcbopUb/LJYqaCkbbP3DzhtTxsjYFykZ5UUiEdQbmptE3QyFQjoff9qa0rJ15XIZ3aKMLS5RhtzmPbtGu78OLUZzXKqnr3NtHYOvHNdIQZlt1TX/Nmx8d3s6LJQfpjf98Pm2tjhXZjJc23o8no4Qjcmfm6y8388+Zkh/VYXOHo8PVTefRVlr2lL1poT9fykcZN8JJ5xwwgknnHDCCSfeoXFbkf3ri+Q/WT1gTUj/0O6DyBS5K4n0sCAiK0pW3Us0JiAjkUYHxa8hKaTd777VLAXahTUqfG8owWMsb6XRFhJZLPEEJivmjXIXtqZaAdu91iplBJQZaNa5qzJ+VqybNQhpmXl94Yt/BQBYmVlAQPcRHSdCGVAxRkvmKK+/yqKNO+4m/z4vLlaz0oDXy3utVrmbuy6EdFCW6kuqeUh0s7hpa2sDfsmilSXLFlGxi6/FZ5Ev8NlUGzx/dzLV2akvrBCRMVksn4p24ik+m7zQrFhqGC3jJft4P29enoUTb0/U2uLHG2pwi4ymDEnesl93tW5lhbtUVeWB6yYv3TCZtploMdxm4tVkG/J6W/C5icREAkIr2kV9hm2+JZS0liPf1tcooVhieyltsl6lx89MVKClIsqKshINvi8eCWB9lgj+pMx8PCqI3Kes03aO17Q+dR0A0J+SNNn6Euplma64ZcKiWoeIX7KdVozvUdau1YK/xd9LkqJtyrTPMmJW19BumYlLsyPd5tZ4U+/w+fnYapJ0a1jtgzcAl5cITVNPudq6VULNiZ0J44Q/+uijAG4iYPPz8x2JOyvAjcY4Hxg/3n5ev862lc2mO9x1C0PE43GOez09PNbCNNFCj9eFtpDKdz1wGgBQ03zx2GNP8W+N8R4fz5/s7kWjyXZh2Vvj8brfYmBkYhCLi4s4cuQ4AGDqBueFqtqfoab7DjCrfPaNswCA1VXOuy+/9CpOn+a1eTvJPLbvnIp6twtEG4MqXu7rSaGvn/13z95RXSvnhwEVLz50+n6eXxK4bpcPaaH0Z159HQBwQRnusAQxBgbZfw39rNerHfnHgiQXz559HU68PWFmjPbzxxkyWUbZfpq0pGW/AoEAKvWbYytw83ttdcxJVVgbuCkx6XUp86q1kYkmtDWuNtQ2A5KWzebS5tmFSsWkPHmM7QIzbVLC7RTMBgN+dGsds13geQz9N4Tdr2PUlV2Dx+YAF6pVZWn1WtOyucqW2UU3WiZF7euYd9kzCak42RgZlmm09Zff7+/UOJjEvF1jWMZ19uyNxeL1BRCNib1R4r1ajcOPCgfZd8IJJ5xwwgknnHDCiXdo3FZk3y0jB+OJNZriakVSWMsQBYwJlS+L12jvzcpUxLhLhUIBGaHYYfG2rELbY9XQQuJ6kqnOZ6OS8ixIqaCm3ZAhKFVVmrt92nUGwnArq1CrNXQe7tgCAVWYa0e4ss5j9o7vRlZKN9sZoip17VIHhimbNnzgDgDAxbOUbnv0wz+j89cA49MJgRoU99Ln4+6vtE30ZTVN9KTt8nUyHzE/d4LpDRkXyUwrFuUzgJDGUr0FlzjNgQh3ziUhKkFlP4wzV6kR+Wp5XChKei6eYlZhSIiXEzsf7lb1ra8AkJyZy/j3hvrzHT9sp81/2GddgNu45Ka6IzSiY/ik/iiToaC/BW+D7dgnfmA1xyxXQH/X1Y8aOcmKjfRhcZp84N29MtaZIVd/3+5xAMDli0T5hofI63XV6uiP8nds85oaQsk9ykxEZP5j+FNukfzktXQJ3cNUx3JL9aSDsshcyyelg5B4lj6PG0FlRtay7CdN8YO9HSkfy3ro2bhc8JjykExXKlJiaHtlJCOUZ1t9rt72oC05wqb6VKXl4CtvR3z+858HcLP9X74sSczBwc5rZjmfENfcTBhNYcNqlVKpBHbvpsSrZXgN6U+niQYuLrKN25yQTCYREGp5bXoWABCJsE19+KMfAAAsLPAz589dAACMDE9gWPLQ991HecyzZ4nG7xrnPLG1xTa9vML2ftepU1hc4GvRBMf09S2impa5mFtglq0sFbmlZdZX3XH4KHI59td4F/vE9A0qr3ktoyUDIkP6H3n/w9g1yWvZsExdLxFKUxfZ3OSz+cpffw0AcPz4nZjYzezCgw8+yOekDFpZKnGvniFqbxmTj37043jtLGWcvyle/759rF9wYufDOPaGxHdU1nCTPWH9ptpRvpFxovjknZov3DTnMulbjzJgnb81dno9yjR7vYhJjjyhehGTgSsoK1SpqI5ANVDJrjDiYnOYsVdfD+u/5mZmAQB+rZFiIb5vcnISC4vMgEVDHINHh9hPMlurumE+i4DU2gx5z+TyKKu2JBC0OgaxO1y8Zo/upynVoVAoiJrrlsN2nudbZUPt9Waz2WGlWBbSxh37nuz/dh6g3pmHPGKLGAPjR4Uz8zjhhBNOOOGEE0444cQ7NG4rsu/ySse4cpOrBAALy6uIasdmSgW24+9Ui6u62uUx1Dl88/cgUYq2kLWajm8qM0UpM3gD/s6uyqq4G+KHbWWIeAwNEcWwXW0unenwpQriUwVEeKyqCtotpZCQdpOZTAZN7Txdfr63r4doyMbGis5DLv+KTIOCobj+nu9wFn0yUBiQScvcCvVaU918NqWCDEpSMWRlOuZpyOBHSGVYGrnVsnhdUh0K+gOIhPg/U+GJJnke29nmtot6bjItCwSBCv+XzkuVQhrOTux8hBpbt75g+r8ud6dNmi586y0K7jfNOPi3x9W2hBHcxj3HW9Rl9HdA6hthtwu1PFHDkHzDc2vk1ncJGawJ0Y8UmW0qz6bR3Va9yzKvf7fUTkrynpjsIYpfzUlRwR/GkBkFXSP6Wsyx7dXljNWbIoIzov7ZE+a17h+NIVNmP6xLB7xZkFlKVchHVahVmeNP2wN49dxiJWVCpDrUQbykpW/KCi6PB+6gagCC7Fvy7oK3i/0mFGQfr+rYlaYLtbq4nkL/q65bFRqc2JkwZPLCBaLmhpDv2bOng0QuLxMdtzoWU98x1Mw4vMVi4YdMeTSeql0YlxbGV1YbWE9nMDpMDntTCh0ZtWFfgD9X13n8Bx48rWvbh5CMIbfS0tmXeprNe4a8ZrMc669cvoYNZXTjQvZzGvsHRsYBAEGhg/econ/KD578ewDAzNwCDhzYp3vlvLN7kp958w0i7SeOUt9/QGo6U1cuoyvG9u7WfHvxIrMB169RhSeo+Xf3bh7b5w3h5Zde1XWzT26sc3x44IF36ycNKLPy0fnKV/4am1KK6+vj92IZSCd2PsxQriElp5CM0ZrNZmdN1FHlEcJuZlutptUxaZ4IhuA31TGtwaJaX5WL/KxbWQCXstUuAFWtRdJVtpGAagZjGl+HNE+YUs3U1DXUS+LVa33lbcvroco+YT6QTVPa8YygmFXGV0h6yMtsmluGde6mmVPJh0X3EAm4MNhvpm8c47UkQ0rZQajOLa81lAs+NFSr45dSV12ZZju//bTwer1o2LOM3coiqem9btWUxcVSqdfrncndozVe2/PjsXsH2XfCCSeccMIJJ5xwwol3aNxWZP+tnCVDH4PBIILidBmSYdxCQ1YGBukOdnN3VOtwLe0zmS1yB5eWlvS6NFLFxfK5PZjdpPtgUIhKSJbMNe1eDf1cXCSK3tfT00E+DQEakZJAfnP9lvPZtdYqDaS6hNhMU+FhUz4BY7uolzx1neoKqQSRot/7vd8DAPzar/8G0rr3tPjEVy6T89wzQHSzJOUiQ6Yy2S24pbASEg9uYY4qEYZ4mQNjNMnzlbeLqGnH6Rc331znbEef1648LC3yWqOOaEIKPdKDtVoEJ3Y+3O1bdXMNzXe3vR1E3xRADPXvuOx2UHvp7LfbHUTfY3x+OT+bDbpX5wsJfYkCSGfZtoMhuSpu8u94iFmzSkX9VCozvlIdUSGpvjAzXahJtabI81m/3RIPeiFfQb6L7SmijFRcCj6RGNGddo2fMeQ/Fmd79taaHdS/IHfGSkN9uSHkA2qrRbMxrwJNoTeemJ6TxiQhUMbfthqeptuNdpB9q2U1PJJB8UsFJdDDY9TaqiFqtDqqSS4h+m6Xg6+8HfH660SmLWNrbpKtVquDkp84QdflcITt8+JF8vrNYdfqwUydB7g5Flp2wNru+jrHfq9qu/oG+rEo7fqIFN5M198yBmO7iLSbokc4HEZfHxHDrzz5FN8zRtTx6lXOD1NTzKT19vB+Vte20BICmc7zehMJtv8nn3kBAHDkCLXrs8osVMocI1KpbszLxyZoCidp3kd/H4/x8PseAgDMz3L+KGSyeOyb3wIA9Ayxz2+qbsHGoKVFcvkPHWQd2oULlxDQ/DoyPA4AiEbYnzdVS/b3f/8DAEBx2+olmp1n3UGQWzc54U7sbHTWKj/knAuwD9i6ySd1s7LG3qBfrrFlYx7w+4qGA/C5OY7a2i6hdcKWi8dqtSxTyjE4Fg3DK+mcupgYTbEPqnJkrhaY7VoSTz4UCKAiPn9Zim8DKfZ3f1sKcqqNasqVt1XOIeTmNWzmmc1qlDlnhZXB9gc4T1htYjjKuWW4fxQzqgWoiKFRKbDt10OqxVJWoq3Pun0euJT52BZLJazMhF+qcPm3PPNIJNJZT3VYFhpD7G97r40p1VoD1bcwZHy+m3UX/6VwZh4nnHDCCSeccMIJJ5x4h8ZtRfZ9UvswpMO44X6/H4NyDmxUuUNqilO2R+61qxusnDbU5cknn8TevXRvPXSILoDju6gDHJUKQpfUN0xZYHxiEhHtlAwZXFpd4zEOkm/oFbc2Lj3gamkbjTI/c/ggz1eWputLLzwPAKiJZ4mG7VC3URB/GDwcqgW5+GZ43rB2YZtywW2J9+9qNRHQ7jiqa0joGfhVTb64QK7k8HC3/p7B2AizDdsF7ibjCXkQqE7CXCNbqo5PZ9bRjHH3aLtGtzhf6Sx31IGQZWKEtKCNphDjsJSCKrUf79rmxP//KAkNaUuD1yskxeMLwGh/VXH7TPs4JhWEbdVSGO+1iRaa+pCn3bjleAHxAQOm5LO5oPOWECkRBanL2yFZJ+rSI4RmSx4TuwaIUG6trKApF84pqY+4O1kI8ZzFfR9NsL+uFDbgkWpNdxeR/IJUf0xmyCU34aA8IGKq6clk85CoF2pCArvkmLi0KlWgCaKlS1LwSSVigMaiRJDPZ2ON9QTd3UQg14R4JjVO1FrtmwoJHtXjtORMukT0tSZ0puZlvxoankRJ19+SCk+h7PSXtyMOHqSz8sIC2+7+/VSDqVQqHTTexrlFIdHmYGlomaFrKytLeO9733vLey1jUJd2tv2clGrP1etTSCbZdgyRLku1raePqLxlnu06rt+4hojG0U9+6hMAgKeeegoAMLKLtSkvnyH3PT3LjNYHP/QRnL/G9pbq5nEjyuZaTcCBg0d0H2zT4xO8xoGBIfT18b2jo+Qi5zLsZz6/cbjNmZTXGIvFUBC3enF+S/fMufK5557TsyCSWd6+BIAKSFNCRFNJXuOVy7xmQyHdQnVj0liPxxIdpaM9+/fc8pyceBtCg2ZIjIqKlFzc7TY8+m5acgaPRfU9KIvvF289LLR7Y3ke99x9JwDg0iXWzFzprFHYzgJBvnd+jv0p4urGHcq0zU6zbZSEhB+/g5mpF59/Qedn3ytm051+/tSTzMrNuHiN99/3LgDAuXPM8JnS3OrcNXjbnKvSa1TlaVfYfltC+CPK9NV1/laZfWEpsw5ofu2XV1PQLW8jqdD5WuwvA0nVKFTL6IpwXqir/a6rFqUr2qX7uTXj5/d7OyyXuPpyUdmTWEx1Yq5bMzGBQKAzbtk62vrhj4rbutgv5zi4xoNatC5yQo0PD+HFZ58CcHPhHldB66Xz/PIaWqBMTDAV+vOf/HiniGrfXm4UVq34tV8LBhUX9iaUfipto6+bC+R5UW8G+/jeixfZSA8fZkPzidswNzOLu+9k0dKLLzwDADh+kOc7eYz25098j1Jh9aotsJoIaLNiLg91pXmyW7xGl5dfYlv6TB5JRlUrebTMolnfjk+9a3GeA/6wzLWee5rXc+L4kY4MWnc37yejhaKlu7IZTjRW7DIyMoRKmdeUljzoYD+P61UVSlkDgFfXtl0qIqgGZg3LGpoTOx9RFU3VRSWpa/CtV1toavHbkvxjqc5Fd8lM1fQ9u0yGFoDLNg3a0AZ8+p9oBVUt3DOX2RcODUThUnH72ADbldvPn3lRFvySZZs+x8HX63JjfYkb8x61xcFBLupXV9kG26K0bMzy7wP7DmAry7FhTXJomhtQkvldVhKE1v9nZ2cBAInuPqytsf2GY1yYn3vjVnrGM09y0kiKWjdzdQY9KrRcdol6IXnORsUMxXgBU3oWkWgcLRVuDY1zEG/V+TyDutjlRdLtEgPjvJdzyxjew8lpW5SiMdHpnNjZePPNNwHcpNmMjLBYNpFIdCgLNqGmVDBukn+WKr/jDtJQhoYG/j+yg7bYf2s6vdHk/+NdETS1AbCC2bZ59Ii6ZZRKO5/H48K5868BAN53mpuLn/05Lvq/8IU/AgD09nK+6tbC/rkXnkc0xY11QePzjekFvZcLkWtXZ3TNPN/ami3og3DpWSyvauPbxft6+H3v5zVKYtBoRLViA8Ui78vv5fOavsE+MzHOedAot0Zn7el24b57WYi7uEQ60PAwx4CmKA59A9oAacPV19+D2Tku+nJ5vra0/OOlBJ34bw/rE+2OSMPN+cCj8c4nOVbzkWq7jU4quUhfu0ON8TW5+N07xrF/SPPGa69z0zoicZFKKY3VmSsAgL4ujtMXFvn9T/NlRH28tqcfJ43skUceQXadm4W5KbbPDz3CYve2CnQP7aHwyQsvcMz/lV/8R53C/PQaf5rYRFIb7Y1VHnNQ9OyMFvKeeh3uOp9FRBubGytsz1WBpjGJl2Sy7GOHDx/G/BLnqrJM9BICpoLqW3Z8n2Sem41Gh2Jr66pG9VaTMr9fgKyefbPRRrVxqzS3jXM/KhwajxNOOOGEE0444YQTTrxD47Yi+7Y7MmmtbaHR60tzCCm999yTTwAAdgnBM2TlyDFahPcKmXe53ZibY1rm9bPcNZZUyDE+zs+GhaS0lJpM9fdh3lL5Qv2ef/opXltMSJ8MGHbt4g5xoCeJ7Tx3YocPMTVcFiVmeZEIyrJSVtUcEcax8RFklWVAXal/FY64hQQ1hMjWZBYGP1HHVq2IVIJZjawQk5roA7Ewd3dLszzfAZnCrM7Po0cFVu06j78wy8LgkRGmg8dGiXTduH5V5w0jrhTq0CCRoproSm2lprxuSXBZhsHdhE/FLi4Pd7YtjyOP9naFp8rMVFs29C2hDHD7EQvJoK5TsMN/FSUP61FfsCIqD1xoKg0bNJqYSYOJitVUFujdR4lwrl85g7ayPy8qPWonSlrmQOng8VFSZSqFMu7Yz0zYyjLRjlSCiMnWJt9blzGImZxMT8+jZ4So4cIS+9TYEFHKG1dnAQB9KbbVC1eI2rtUPJartZERzcCVlkW6JHCnlDlIihq0tKzMYqQPhRLHoi3RGLp7DLbi/RotLpxgtsvv83ZqoCsyzAv4VLSZIZJzQEWUSyuEpg72D2Hz4rMAgJERjicoE0VyYmfj8GFmWdfW1vQK+0qpVOr0Act2baXZDjc2OF6bzLMhY/F4vIPsm9SmHdcjqmO3hA4WF2Z0tjYSMY7Bx4+SRpOX7OSsil3tOvwBHqNcKsD8ip56+nEAwMc/TnPF+UV+xihqaaMcxaIdRH9NctGJONvdVobz0uAQ54XlzfixSgAAIABJREFUVRngSRpzZmYGiQSpqEbrPHUXn5vBfkb9OXGCtIwLr19CWHTYlXXJgqqIvlfSiE1lrY4fY59dXlmEy8sMnVdjTF8v+1G1diulyiIcDnbmXMuiLCzMwYm3JzxuM1+0V4Teu9rwSKTALQEPEyhxi+3g1U9jKyRiEbhrtxbVNtwciyNDXOtFwTE6Kvqyu9ZAeoXfb6MoOrFRhHX8aIqv33cX136ol3FSLIvcFvujSCIY6Od5VlfZ7vbIwDG9vortLOfRsERSYqJHj4h6GlYGbHaG66rRXfzsiy++iJ/6GDNtVfWXhCjiC6L39Ugqs6mC3UJ6HS6tN30u9rvuFOc2o6xldT0RXUc+k+6MNyZP7/OatDbvz7IAXt1Do9FCS4X3bo1blm38UeEg+0444YQTTjjhhBNOOPEOjduK7NdL3B01K9yBDPZJXi/vwWHJhT3/0osAgH4V2UW0c9pc505uY407t3qzhaS4l2t6LSW7cONERoQehFQQWCwWO+iNIRgmdRZXIZ7xOdObREXGRoaxukROZFOc/PUV7upM0jMe5zW2G9xdzs9OoyusHZgZb4lOFfSa4Y5QWskEeoTad3dH8P0f0ARlfonnefe7361nwPvcv5/oTFNZg3I+i3yayE9IKEwqKUlBQUNrKnC2gpzdk2PIrBP5zIkPbciTSUd12X2JfOrzuJHb4s7ddqD1+q28MSd2LgLrRIgDajxh667uAGJgu4n52Oaagp0LLb5er/I7M5QMrTYqar/eokneKtsknnp1k+3tOy9+hx/JFDA8yDawq58ZIqjdmtxlZkPoYYBIZ7XqQSRBhA+bbJ+GvG9kiQwal7+qIt9Mdh3NLba5RB/739k3yd88fIDnXZrltbmEcHTMRqoNBOJ8BlUZynUPsSBxfu0NAMBwkseYmmE7d/sS2FYtgifMfr/BRwCX8I/SPNt5SgjutalZ9Kb4e2aL8r1+mQx1K9tY1bjT383ryS3cQMzM+S4yoxiU1K8TOxtvldG0MTmbzXYKq42fbLxXQ9qMF2vFbx6Pp4O0jY6Sa96RGhYv347fleD8UatXsDDPOWpzTf1KtSldXTyPSSUvLRO1rzcqSKkgPChkcGaW7f6TP/vTAID/54+/BIBmPQAQjMYh/zd0h9keTxy7FwDw8kvk//epINikRLtUy/Lscy/g8BGi54asv6Ks+AceJme/oezhnIoZXW4vkjLyqbY4py0rY1dV9jAvwQrj2g8NDWBCohI/+MEPbnnGvcpAG1ffMvdnz77eeU723gkVFjux82FCJ709/G5HR5nJHOzv7RS3psVOWFnm+icv47eA5o1cRlm0cgA+1Yw1t8nWuDZLTr2nwn4TFCNgdY5jZ7FUwPFjJ3k8FYOjwbXLgOo1bY12x34W1B46dAhvvMkaKr/WKn/4h38IAPjIRz4C4Kb56rPPPqu/S6hWbzXNs6L7hEzpXlIh8C/9yv8AANjY4Pt++7d/G8uqP5ueZgbP1qH797BtRiRyYsat2c0N1Fuch0oyLIvJrNQyC/GY1f9wHby5ud7h5Ec0X1t9T1nrx7bkUD1QZsTjRl0ZelvT2md+VDjIvhNOOOGEE0444YQTTrxD47Yi+yb5l93k7iiRlNJAPI4b16hkMSh0fl3veUXSY0MjRFC2S0QrAsEwvEInalKEMdOhrTSRhhWZDtT1mXAggLDQnEQXz53LcyfY0mdX14lamKpDrVZDV5LISH+SqEhePN+w5Mp+5mfI6/qd3/xXAIBgKg6riw6rWrui6m3bUXuUbYgJ2WkIsVxdW8LxE1QkOn4nsx2mdjAmhYmqOJubyj4k4kmUxLcuqb4gIYS/WOD5qnoGk2O8h+zmJq5elFSazFK2xEs1tH58nOiMoaDhaAT5Gv830MPvqWWmTk7seIwXiGJ4PUQxGpJ6LFeaaDdlcuUlEtAWqu0u8fvoVabK0AO02igU2DYaZgCirb5fGaK6vufj42wjo/cP48nvE5nb/S5ye0315PxlIh296q8vnWeNiMvlwpMvE9U5eoSfefUq+/aNG/zMMaGv5y7wWCfuPoIpIUGxmoxc9AyqgjHDygaUSrzWgJ/31XaHsJaW3GyS7fiNqVk+N3H1X71ItLQrxf/PbWQQlNyamfoElIkzVAuSLl3bJrKytlyBO8hn6goQEaqpBqLRlpyg6ghiQmk8wTrcQoglpIRo8LYOuf/dxBtvMIvTJ3W1XZJhBm5mtwxFzkoa1sLUOoaGOL4mk10dZRnL2pisZlFjb6XC8TSVYN9MdSVw8jilBH/6Yz8HALhymRzgf/vvfhcAkFEGdXRYHPvcOlwytmvLfCgaVyZOmTobm2s1/p3L59GUOspgjNf0yiuv8H8y0frmNx4DAJy8k1znjgyvp9LJVCSVsXrPe04BAHya/55+kv19pI9jwJvnr0EqpvBqLHnwobsBAAckl231DOfOnQPAWojFpVkAwMQkz2dIfk6KfC2ZQFq2w7LKALC1yfdYBsaJnY8+qTyZgdbi/CwAYGVhDrC6Qs0L/rayrhGOxZVtfYdVmdAVt1BocO645yhR+KV5joH93WJxDLNv5WTU+eqZM+gRl70rxXZ8/Roz2Sadbhx0azu/9hv/Co88TJZDXspxd59iVqssQ8W2Vl7HjrON7jlwCGfPsN7s9Ps+AAC4fJlzTbHMvjetrPFj32ZGOyzTxPWNNE6dYv84dR+v/5uPUXnxV5QFOHPmDN8rCfcTJ07Ao/H/ygwzE00p7KxL3jmgup9okM+zuysOKEsc7+K5G6rTs+xcuay5Rmtdr9eLdsOkuWUE+V+RQXd6kxNOOOGEE0444YQTTrxD47bCTEUhi2asMTxMnlg2n0NC3PwNVSp3S5Fm/DS5UbkCdzib4qbHkknMSOXAIx56Q7srUx9YlxZ4t9D8udkF7Dsg3WvtmKLSvb56jVwy4+xHxKH/t//L/4bP/9r/CAD48p+TP7l/N1GPL339rwEAp05RuSAg6/NGuYxt8T+7pADh1o6sKg59UNr5fpkoLF8n+vjVr34Zy7oPSLUkFOH1l8Ub9QVlGiYu5omjx7B336Tunc+tpPqIQye40zY9/KKOkUmn8YFHfwIAsLpEZEty+p3sg990YOu85vJ2AUVpSBdz2VuO68TOR1eN/cXrVQZLfNp6poJMmt9RsWga/Ny3t4X+l5RRMgttr8t90xtBagECCOGSwVRTigzZfESfCaJald54g31sXSi6P0hkcHotq9elBDI8gnZMfgAhqZ9IVzg5TpSvIX5y327+vVWpotLmdU5f5nvvP8V+ev4KswEH9tFoZ2uTXGKv+obH78H8hvw0ZGZ1dZp9eXCIXP05cT9PHqHKUCnXQlQay1uLzI7FhCKaWsiQPCdyBd5XamgYW1U+/1iA95WI8xhrQopb8j6YmSZ3eri3F5vSMx/oVz1Rmv2HuJoTOxX33kuEz7Kg1tYTiURHbSedTt/yHkMOjZ9v2dxUKtH5n81VxnE3dYxEgoj0yePjAIDRsSHMTPO7fvy7RNYjYc4l//yf/dNbrunP/+KLAIC+7hRmF9lW0zmi/oePMKvbpayU1axtzXJO6Onpx4rqpizsuPfddz8A4MYU58WBfqKR16+dBwB4fFX8py9xDjNVD7t3SOd7TejjcD/nuF//p7+BfI5jjU/Zr5Ex9lvL8jVUP/NTH3sUAOePv/gy58blZR7vV36ZSOhXv/pVAMDqOu/B+NOjI7uwtCTkUyolu2Qs5sTOh5ktmoa+T6pJPk8bFZlLlVSDkSlKFx5sZ/sm+P2PD7B20NtqYGOVvP7JIY5zQRlZxSPsRz1xjpmmblU9ehTXrlEZsF8ZNWtHC1JMNNNU461//Kc/iJDWUwMeXkNW43Msyf74wAMPAACefpqc/WAkiXiKfWl1k/3/vY+wnVo9ycd/9lO8D3HgZ2fYfzbTV7GVYQbBMlEPPPAQn0mW5/3wh38KAPClL/1nAMAHPziMqWnOUYcPcb7JFznfWn/p6uIaraB5ozuV6DBWaqqDCQTYB3zm09SZuzm3ul0uvDXxVa/8+PpJB9l3wgknnHDCCSeccMKJd2jcZgKp3MjEyaqJYzQ9PY07T5Jj9f1n6Ao7NMxdvT/MnVxdKiBNab/6AyGkerlj65ber09oRbHMHU5Xlbuh/doh1hvAK6+Sv3XiTqLx2RyRtvsffBAAcOkSeewuH4/1m7/zbzpOa+ktooC7x7kTfexrvJb7HyCPbErc5K18AdC5Kz7xqsTT8mg71jAxHu3kzIq60arjF/7RLwAA4toBZraIXO6ROsF/+N3/CAD4wAc+BAD467/8cgeBisWImhrP3mcqLTn+bQ66A9192L+Pz+Wv/vNfAACOHaOGbSxChDSgZ3DyKF/3+/0dpMm+Q3ic/eLbFeWW+Pbi6ypZhFKjjZy461sZ8fRcRB6DQp03s9LObxqK70ZQ36dX1ftN8TXVLeEyqN/PE124fgZxuYy++CrRwTcuE327+z4qKVy+QmQyEmf2aa1cQVNutC9cpkLKtnjOBw8SnX/qDXKMDx1i1uk7f38JeybIY+7p4/mmZoioDPSRMPzsi0RFxifpoXHxKvnQY5NheKNsr3mhHv4Er2V+gwiRO8bnN73Oa63Xyygss08Zn7veYD80pLMgpCWb589KENhUPU9UKO/pdxNFarnZJy5Lp3lQ9QUXp9cx0McMwewa78fUfZzY2TDk3dR4gkHNBcViR9fa+K/2HnNjNl6+qXXU6/VOLYr9NG45hPDt3k3FKY+bx97aXMX5c2cBAFPXmFEOC9m/8+57AAD7lJ26717OdUurs6i2zKWc4/Ybb3B++vVf/58AAI99i/r7ays8j9fvw/sfptvuinT0M2kqhlw4xz7agh2LfwcDcvvsCne48SYGVyyyXdbKHCd27+U1tjVvPPPMc525cnWdiOVmlj8XFnifV64Qof3Bk+Q8f+ITn8QHPvg+AMC3vsXXvqYseEZO2QcPsl83ZDNTqdRQUNY4LX8C0/N3YucjKCVBy+JvS1GpXa+gXmY/adb4fcSU0elWHdi+SWZ9vKozcTeruHGR3PUbV1hntrIwCwCIa71w7SrngpqP81MgmsDKCtutX/WLE+Nc39TERc/meW3Gww9F4x2GRzTOTPDxO+8DAPRoLfj8S2c7xweARtuF4VH284DWkq9fYHvtGx7nM1C7Mx+O4TGeb2pqClUt1Kxu4IYYGDmp/vz+7/8BAGDvPrbn+YVFjI3xuMkejhFtZQzmZjn/dKtO8+WXXwYAHDh0GNjmuFLWs7dau7AYJpZ1sAxD2+Xq1LS02+aZ8ONZFs5KzQknnHDCCSeccMIJJ96hcVuR/SVxaz3Smi4tEhkYHBxETVz297znPQCAoNDBb32PmvP3PcDXF5bJXbx89QZ2TZCnbio8MSHhWxkiD4NDrAm4eo2IW6FYw73vIgq/tk6OZFg6qVcuc8cW0rXZbvLa1akO3zYUJmII6SffeS+zAd29PE86K6WgaBfabu5KC3IgRYu7Lpd2j6ao0xSw75Vj6D/8zKfxne8SzekTKmgqJmWhL7393MUaX+3ed9+PWIKIoWVLqjXuSM++zh31nolxAMDp91JP+Y/+8AudLMbddxNpMl1bQ3+2xHHt1EBsbaK7lwjXgr67mtR5/snPfxRO7GwUXUQv6qqZaNTFLW55URE3v9Iwpz3+9IfF0Y9KOaEgL4ZWG02P8Y6JDmyX2X7KFbZRgX2IdbFPFNpeoCnNcOPuC2FYzSlz4GefWFUmYf/QGK5cYpvr7yey6ZLyTWyA7XbuJfa1Pjk27zrQhXKBZ9+1lyjM2TMvAQBGJok0bmwrKyh1HF+cxy7Ci/lNjiuBCJH9sp5XRWNKRDUC27rvagsQkI+4eKiNKt+7a5So1Yx4lyY2lWmX0S+Xxr3SfV7K8fqXV9gXLFuXlffAWO8A1pRlDEvJZ3PbqXF5OyKtupAeqYzktjkWz83NIRrl+JVSfVZ5jf8zbf6hAWaV2movmfRWByWLyV3Tsp6Qu6g/wP6wvMLv+tz5q5iZ5ZziEXpZVJt65QxrOIqqCRgc4flCUT/e98jDAIAv/PEXAKCDbv9v/+u/BgC8+0Gi+NM32B6rlUJHeahW4zWYG3tPP/viQC+z4lXNBQ0ppbjcFazJu+WTP/dhPp9pKpOsy8Plox/lOJ7e5Jjwgyeex9nXidpKAAtFjRfGsR4e4/ky4kT/8R9/AZOT7CM/+ZM/CQD46l+Rq2+ZtDnxom3udrm8eORRvnd+jtdYqTkeLm9byHPFXefPiIftOhQMIpASquzneD3Qy7F2SL5IZdVeXrnEttOolTE1R+R7YFDKTxH2tYjWMKtb/EwkQVbE7PIKBifJae/qZ38oCLXfc4iMA5dpGioTXSpXEUzwOuPyfsirjiq/wHVh79A4z6NMs8cdwH55S7z2GrNmPQPSxlemLyinW7c5bSelAJfI470PfxDATeXDcu27AIC5Rfk9aa6pyr062VvG98SU+Owv/TIAYHIP5zCbP3rkI7CxxnZ+5NB+hLy8D4+YK27VjkXNz0g1FWXV7dVarU7faSobbeyXHxW3dbF/Y5lpUn+MX1QsykVAOO7vFOv+5V8x3bdrnJO+pVZmZjjYdakQI+kLoljiYHBjlsUhN6a/CQDoVhHIuNIpkYDkNhN9SEl+z4wPapKZnJc19513UT7Nih/Wl1bwt3/zdwCADzzM1OToML+8TQ2IjZoVt7CB16t5hI2q1ODgnRxix8mk2eibdUn8aSHtlrxif98w/BpVx7XQGVN6q2Ph3s3z+CP8zOK1BQyMcLNQrfErTQzy+TV97AQNSTQurbNTtn0+XFZK6vhxSnx6zdjL5ND8bPSpEQ7mq8USvvccF2F79lF2bUmd2Imdj1XJQAatWMfHNhuN+LCswlh/UDbdBQ4OlTS/71hK8n41LkirAFqS4ItJRnCjJV6QFbir6HVZEn493UM4pyL4wSbfE+hhP332dcqkDQ+z3blk9vHa+esIqug8sy1JTxUEP/0C204Px3tsadLo6+/FUpYLZo9PnwF/rqS5AU1KrnBRhVE+Fa2/MTUHlwbCFT2TVDc35ytm4qXOnFvXYmugC7kqz13YZp9KxLhIvCAKxkgPx4nMGhdwAb8bQRUOzq1wvMlWeHxXgM8mrYJq1DgBFD0uHFIK+foVAg7dAhec2NkwQYS8qCUmS9c3MAC/Nqjrm/wuT54kBe3Vl9keZ6f53Rw/xsVHLBbB0qIKDsfY7uplti2XZO4ym6K6XWOhaaHaDU+YrxltyFLv2kPgpddJq/FfYHs8cfIIYhn2tUd/govvb37zb3k/omHOzpAaur7Ba9y1az/6R7gYeu4FbiJOHr8LAFCT/OD1q6/pvOxD0bBP11PD3UdJe9gWZWl8ZBwAsKZCWrv2TVFHl9fm0XJzXLhwmdcyNMr5YI/ANqOCeH3sk6mkC2nNCz94gkWQ//iXWKCbEy0uIODszQtcMD727cexpbnx+vQsAGD/fhbpO7HzcfoutvViifPD2ZdpLNWXTCG9zrYwNDkOALjvOOf6L/05C8utjQyqsDYQT+GhD1PWsneAr73yGmVYT72LVOPENtdZVySLPHBgHL/4i78IAPj2d0j1uvfIEQDA6ioX0pcucY45cYJrsvzyEpICdE1Ixa0CVjPMs82kmeDNTC8h2MUxuOXjvNAOsF8GfTxGv8bksuR0PaLKlJt+LG4U9D924n71l/EJSsFPTVFyOrPFjW62VMKANr8vneMm+fHnCFjP3eB6K6Hi/6MHeKyeqAcBbdwLWo96Gvw7KCGMOVH2QgneQ9PlQ93N+8h3ZDlFw/0R4dB4nHDCCSeccMIJJ5xw4h0atxXZP3CEqdBwyJBwvu7z+zvGHOOS9Vpd5e6yJPOEPfspSVaqcddVKJXwmoqZHn2UUkp9QrxzMi9JyKBkqJ/o49mzr6FQ5A7JLNTLShV6VGiay/I6pme4C9szMYlP/gxRl6mr3Gk+8V1mENweK6qQVKVMElpND7YzvP5ggpmJjGgvZp7gknRbUAjlPfex2G9hYQl338WCrqZ2mA0VHprRixWjmT1yo1FHQzShsIpo1lVMmFex09QU7+eI5Afh8mB4F3eneaFIZtgA7ZbnJV06cYgIy3omiz5RoxouvieWssI1J3Y6LNNSF1LcEFoZ9XkREr3A1eJuvqUiHZeXqEHLK0lOD39GEkksKhXZ1cX3FGV3Ho8TPSiqrxXy7BtBVwPtMNvrVpko9lg320y3zH4KSumbIZEvFER3D4+3ssqswO49LF66rEK+g4d4jI0M+2mxWkPTLWlYZSLGJsd0H5LEFH0iV1W/LRhNrgZvUBQmSSzm1B8HhomCmNRiz1CPzlFB08+xweXjM94QOgmlZZOSOqu1JLcZisKAk7qQTo8K9xe3NFYpjWoStmvbOfSr4KplBVZux1Tr7YiQZF7vu48mONdk0jg9dQ0H9u275b2PP06a5Nws0fI7j3NempqSwMLmBj7/2U8DAM5rjhkUldMoNEPjPGbZ3N98CfQOaFwOE/nM5YmeVyXC4JHpWluF8JcuX0epwv8dPsT57Vd/9dcBAGuSbP1j0XtOniDqOTe3jk3Rjx54F+cMq8uLKUvsBV/wibY2MsJ2Pz97DXdIerpPRkaPfYuZhPe89zSAm9SJF158kfc5MogXXyFCefgYn23Az2ddFho5t0AkdnSQY8D0D50nrQzBn/zpnwIAIkL0p0XViXcRXU1ncghoXuvuZTblx5MSnPhviWee+BYAYEsyrg0hypODKYT7meX8zD+kJOXv/ft/BwDIZ/hdDskgq1hjOxsc7Mbd7yLNOq0M8//x2V8FADz7Ek1RVyTN+Uv/9F8AAM68+hpeu0R6cnJgnO+RrPPd95NBsf8o25utr7qHJrCsNcncMucyW7cNaV2S6OZazx/iOmgr18DSClH3SJx92GRmrfh1eZHzlBnNVSVhGU/2d6TSt8v8OTjKazXjrIjkz7tEK0qn04hKNn5U66uhIa7n3niV733sq18BAPzLf/bPAQAXzp6FphQklKk/eAcNKWtC+AsyfyzVuRasVFtoy/zLr2sJiYnxo8JB9p1wwgknnHDCCSeccOIdGrcVZjp+lLuV732Xu8pklOhabnMVD59msesLzzwJAB3zq+EB7samrxNVH98rlPDqFA7sJZf9uSe/DwAYHOR7EyrIKue5+/ub54nkTExMYO9uHve552m6YIiGGSysLHNXNy6uZq2yhoUtoo3793P3ODnOz5w7S25kJK7dX4E7RLfLBajYw+vRbkuGOxAqIqAS7rZs0WWAMntjBhXt3gYHeL6cdtRRySqGhN5fv8xnUi9XMCXUtKSClck9RJ68QnDGhlkzcE3ZCY/XhbyMsaLiuVnBlWVZLHMwNzMLAOjt7sZ5FeWExMve3JABmBM7HjGh89tCnVtCUgL+APw+ZZUkn1lWQY8oxSiJf+gTUrCeWUdYhbKzc0Qwg8oOZHK3mvT0D7A9lEqriCeI9FWr7ANrG0RjWkK8s1mT/ix3Xi9ViGg2m0THrbhJdVZoyQAsEWcf2y4UUFdmYkGIpl9tfX5ulvclXr7Ly/dFhGJGuxPIFHhtbWXCzJ68rayGW8VnHmXiPF4Aem7+BJ9BviK+fw/v3Z3gs9LLqLqb2BK/1RvRtSiTt7HF47ssU2nlOJ4GWrKal+opPH7904kdjaYytNNC52OqmZrYNQ6PGp7JZxpKf8/d5O5vrXMMa7f4mb17dndqxeDmZz0yp7NapUVxaNM5trFgPIlBzVUjyiitrcsocoMZrO08EcaCxl1Ewrj4JqUKX32J9QOf+tTPAAASQhlPnSK6OTHGIsO/+/YPUKmrju0GpfwSCRWr59k+D0nWsqmMwoayvCNj40hLgjmoAv6EDIesv62t8xq9ynxtl5u4516KWtQafBapPqKn169wLti7j1mJmhDRu++5D709vKay6upe0v0Nj3I8MlnpdRX1njhxrJN9NkntFRW+O7Hz0Wrye+mRDKSZX8W64ugRH/5f/uZvAQCeeoq1Jh/9GNH7e+57FwDg4hXy1WOxbly9zrbYN0w02zJgd0u2NS1zqr/9+jcAAP0Dg/CrjYluj7LEDB577Fs6LvtAQbVdiUQKfkmGjozwPDbHZDKqwdJcENcaqb+/H+2m5CsDQZ3HZJZ5YuvrVgdgrA94WnBrTrlwidLP+/ZwzWlt1OvlwF4qMfO9tp7GXZLaff1NPoMzrzC78T5JNd9xgOyK86rh+e5jj6Nf38NPffgj/OwrlBCNijnRUnY/keJ9eUIuVFqq16tyvdi06/4R4SD7TjjhhBNOOOGEE0448Q6N24rsT18n+nzPSXIkl+aJEo4P9SG7Rd5fqku8vQR3fWe0+9l9kJzF1195ju/rG0RRZiIpGUnt3UXEYXl+FsBNObGugEyJipkOT/m976a9el5829MPUH7SdnUVcZETkSh2DY4DAFri8xrn65UXeC0uIfH3yizlpe9+D+EeojvbkufzJsmDi0TJWcylibBUPNzNmulVNpdHTp+ZlzzZqAzGzAAlJU7YLqnk5De3cEo76C1x5ppCXpvKLFy/SpnN/btZmb6+uoLxMWYOKpLhCmjXbNy1lTUivgkZY8zOLaC3W4iNpEOTMcmROrHzUZNkXk0qAULEfa6biENFiH6hLMmuINtTxaXaEElxNqsteLySnpRUX0yeNZ1SDXYjjE8aP3keu4aIesSiROGnrpFr293L733vAWaklpeJyE+M7saNWSpbHdjLTN5LLxHhOHCAbW9ujvzNA+IpX3pzDgf3su+aHXhBqORatqzz8X7qbRlnCSrfLufRK4nPtU1mFAytmp9nXx8cVC1Phv8fGupHRfzQSFKKVhuCl6J6FspoRPt5//FUEhkzZyrzGtqyhDcuvyEnJsHZPRBGLMXP51QX0/JoDHFiR+PiBc4TW1LcGRsj8ufzehCQyRqUyVr6Z2vtAAAgAElEQVRXPdj4BNFyM87Kyr7+6LHjyOY5vg0McYydk1rbwYNSiJHhkz+hzFq9iWaL7UHqeZ2atEScqGlAKR+VRCEY8qKsftsVZ/v/2tf+BgDwuc99BgCwezeVd2IyBGo2GthSpuzIHVThOXWKc9mrL5FbP6G6t29/6zEAwJ69fBY3bsygKs3ZuObZn/rYTwEAMnkio9974ikAwORuZgfWNzMIimef3+I4tLzCZ+wP8vXNDJ9VlxDS6fkZZKToNTtrRnYcZJLdHEfmpPLVP8jan0qtioT6raGkAdX2ObHzsXc3275lvS5fVpbmwF5kZRiVlGzmH/0ZVXPOvclakUqTn+mTMk1f/xAqqidbkiRlJCelQpmm9WjNsrHFsd/t9mJbdVclcfKjMvOMqI5xQGyHpNTj5ucW4VFm15gSsTjbXCSs2i7Nh1lJYVbrDUSltBZQ2ntUalKWFdgvhsiGWAoe1dRksy5UpfK1ZzeZEpMydVwUz9+yBOPKvJXLVVSltHhgHxH8JcnIXzjHZ3xQa9kesSPuufsefOYXaKT6h//37/Mad1HpyqN53AsZZwnhb1aaaChr7JKKns/34/uLg+w74YQTTjjhhBNOOOHEOzRuK7LfZeY2GSLGJXGFk/vGEA/JEtkn/qvQzLuOEmF46Sx5T/c+QG7/7NwSBpLcGY1I63tTdt7GdY4Oku90WDup8xcuoUtqJTMXyO3skiZ/h4+YEB9+nujF1cVLHe3WyV3jAIDFee5Ojx4g5/Oe4+Swff1rXwMAdI/tR0ZV7gb7mfV3JMTdaw68v7ZQ1oR2tdcuX8WdQuljQs3Pv0HN2pERIvEQal8QwjOQSmHqCrn4yX4hJaYQJBgppvqImOoZfB4XakJPs2kinl7t8pvKYJSVScjo/5sbawhr121cVrOad2LnIyQUuy49/GZLJhqNKqp1GeYI4fdF+L9ggt9HtcLPpPUd9u8awqXrRN8PHSSSubrMNrrnANvMdoEeDLE429fgEBAI8jyxLiIoAvPQ8vC4iR5yF9fTMsDxF9H2qMbAzdckQoKQfDUS4kaaaVxv/0zHfC6aYJ/elqFbrIf3FU6y3RZkAiPbCJTyFYx1S1u5wr7WN8i+tKzak1Qvj5nNE5kPx33wlaRexe4OtwyDym32ibqX154c4vgwMDCE6iI/k1NWK18iepTqk2dHVTUCAu/H940h1iXu85aut+r4Urwd8bOfJNf9zBmi2zZmbayu4FXVvLzv/ay1Gh8fBwDMz3O+ML6yzQWzs/OdrPDGhsZxoYJtF+cNyBflusyuwtEY+vrYF2zc9ipN1GpWdE1qN7ktvR7teGfMin9vKltmQvXBD1Jp7sIFcvsr5SrC8t149pmneCkqFrlwnvNEW6m6fvUvQ0i7u5O4cJlZts//5GcAAPJexOvniToOjxHlbLt5HStraZTr7MBNeWmUKmz3/eItB1Qf0XKz//X0D3XmqN4BoqjG5y8LKS2JZ9yQB4bb7cWeUdMuJ3ffjJGc2PnY3OBc0FAa8nd+5/8CAJx++FE88CBrNCoycPr9P/0SAOCTP/vzAIC0vE7c8mf57vefRp/09T/3uc/xs5p/TGHpD/6Yakyf/uVfAwBcn57D1avk/Fub/7tvfRsA8NBDXOOtK1Ob17jdNzCIjLKzOZnPQW0uLDPUkPpyWzVg5XIZXappqVbZBnM5znO5PH+uat2zJe+JSanoPPTQQ7h8hYwIl1R/zIumX+26rvqwZXkDlKsNpLy8Z8schKSV3/SzX7ak2hYM8pqfeOIV7J9gBm+PMmp33immidaHhSKvo660YcjvR0RqPPLu65hr/ahwkH0nnHDCCSeccMIJJ5x4h8ZtRfZL0rBviFe1bxd3g4//3Vdwx0G6xe6TCo5HXPZYgju10V4ii+0y0ezemBvBoNQ38kRf7jtKXtVTf/8EAGBEnPY3X30KADA0MIzMIneTRfGzthaIuoyMkuf48lOsKxgfJ2dq/up5HDnMGoM//N3fAQB8ULr+SzN877JqBS6foQudPxZHS1blxnvLZLiLND6ax3S3peM6Osz3/ecvvokjUlNYETo1KUc2r7hk6TR3unfsIwozNzeHWFL6+jqPSZ8sS5s/FODf66vkXIeCvg6yn0wQCd2UW2hZu/G+HqLENdUv9CQTWFoVr01V6C0j5Tmx4xETStEUwpKX4ki+XERWygV1IYy+LraroNxjDw6yTaalj58a7EbXKP+3fy/5helNcpcnR9j2alLcMSTy+Mnd2NhgGzQPhuEx8iezUv7wSuHmxN1EJtwuP2JJ9sP1NX724FH285l59r1BoSLnLxJlLJQKqIfZPpMRojDNvGzRe+UBUCWaFOgSV7OLaJA/mkS1kdNrqlPw8PrHJ4l8uL1s570DcjRFEQJH4VIW4sAhKV0JvUwE+OwrcjhNl9IdmZ2eAen1LyvrkWT/KRV5jQnVSaQGkyiIC+3tEg90TYiUEzsaN66xbY1JA3xlhW07FArh5J3HAdxE3M+9ThW1/n7ONQNCJaPKes7NzmJ1lWOhucP2S8EnbVxgjdv79nKeCIZDHTQzIxdrv4pgens4Nu/dMw4AGJJHxdmzZ/HmOfYBv1/eM1L9mZ4m0v8nf0JE1BzmF+fm0TvAcf/0A1T3WFnkmP7zP0dd9GvXiIw/oSzHhDwravUm7rqH/P6QePgrq8ygVaX0lVfNVyjC9jo+uQ9Veds02obwe295FkFxkp9/lecbHRrGqlS14nrm9qyLcvs8dISu7Qu69lAohE3VW+S22WdMecuJnY94jOPde99HTftnX6A64Yc/+pMoFMUFl5rZkGoWc2oHLS/H1VgXx71HfnICX//61wEAv/lb/xoA8OgjPwEAeOppqhwmutnmr1+nt0U6W0SXFHOamlv2S+mqK845wFDzUdUGbG9vIx7j/BOVp4pPqoc1ZYxcPrbNvr7ezvsuSqnQ3mu1OdanzBE4LGfb69c5llSrVczIzXmPeP1uZdFmVIsSEE++W47rXYkeLCyQ+WHc/2Q3M2AnjtEJ+AnV0rx2jtm6B9/7Lnz/adZ/7pngvfarTnNVSlqWlai6+Kw8wQhCWkM2lPmvKVv2o8JB9p1wwgknnHDCCSeccOIdGrcV2d87wh3OQB93bikp7nzqQ+/G/A1yzvuljPC3f0tnvw99/AMAgLZQx3effggAsGtiD65ckaZymMeZmSai8T//2ucB3NSSNa3rzfQG9u+Tzv4LzwMATp4kP76inWGl9jAAYE1KNJ/71Ptx8iS5+efPfRzAzV3lb/3WbwMAfu0ffwwA8L2/IwqzvLQIX5T32pa9oWm61oWmQ7sxl3hWTWU7UMrCI1WHeIRfT3aLKEkhS9TeUJ/Zq+RoHjp0CBde527UH+Nuu6hdXkrKOnkh/h/6xE8DAM68+jIaciN94xxdIidUkzCxi7vKebn+Hj3MqvLF734Pe8aJEr32OhGpSIDvdWLnwyW3ZUhjvimkpQkvXELmYlIqaAb50ydn5oCQyKR8KOvtCvaeIMKJJtHqESF+1RrRy4icdU0FKhDpRTzJc6fV9oyXHO9l9mdV/WR8nO3g8qVrCIn73NrkecZGiYqUdZ7xfURSl+aJvvaP7kdOqHi8XwofRaIwkbjOc4Pnn9jN8xu/sqenB2nplofikhPyCY0dkYpInsfuG453nm2/1E1cPvaBI4eZTYN8Kjyq4VnT89vO59EUGT8Q5HjTJafglhxLAxGef3z/OA/lqeDGMseog7uYTdkoOr4Ub0fkxcOdm+UcYB4h/X09uHyRfNeE6rHicbYDqzeamKDCxhXVPWVzBYyOjQMA2hIB30qzndekKtPTx352/SoVSkbGR9ESn7elMb8rxfZnqOP6KsdTt9rLwf37cPwwFavMi2JUGeY/+IM/AHAzs7Cxtql7SCESYJ+sK3tsymiPP/E9ADfrdT7xiU8AQEdFqumqI6KamFKFbfnceT6bpRXOMR45Snv8vPb01ibuOHJC772s47E/mYqRubV39xC9X9tIY0LqJTWp260oU9KpZ1AGo3+IGcgrV650fBCKRY4blgFxYufDnGfvOMHv9q+/TFfXjc009h3lmmh1y8ZN9o8V1Zy4VYTVNcA5Z/eeSRw4RMTerdqWTfknvP/0+wEAkTj7WqVS1/l9qGk+sCzcqVP3Abg5Xhc1JwTlqRIIhDpov3Hy7X/dymhb33arFiSfz2NggO2oLNXBpSVe2/omx+JW49Y1WlH1JetbabRVmzi3YBkozhs+ZX5Tcs6dn+ec2Wq1MCJWiM1RUTEnpubYx9776IcAAK8+84yu1YuGxpVsif1lXjUAdanvuJRBaIvvv5XNABob2sLs3V5HjccJJ5xwwgknnHDCCSf+u4zbiuz/5Z8Rrfj4T9MlLLaPO6BsZQutolDrDaobJEPcZSXFy5+9Qp7lg/cSnbxx/hVckfvgKTmWHZ4gB2vmAt87PEoUsFIlsjEx0IPlGTqTPfFNVpgf3cv3ZFT5PSOFhiNHeZ5sdhmzV4TcuflzfYmcrIfup8pPIT0LADi0V46g22m4/NwRZpe4I4xpd9kybe4Wd3BhoYTrKzxG38gAPvYR8t0M7UkmiCBOT3H3nDtOfXLLMMS7oljf4O64Jr71/j3cjddN9aDCXfnrr73Ca86lO5zSw4eY7TAVB3N4bMo/4MrlizpWBZtCp0K6v4DDq3zbYiMv5QpxJSsg6haKdSM5QhQs0k3EuBaQ8ofUnoZVz5EWOhKOeBHwEyXo7hJ3cY1t3adMkqetNpkm+pbNZzAuX4a60MhChRm2UJQIR6VJdCEnq1l3yIVqm6/1jhJJLdSJpIztI4qXr/DvgLTs4QJyqkNpponcmCpOX5L3GecPnDjFPvf0k3Tajia9qEr32ec3ZQYNa275CYCIR0CqD9FwFC0Xn0WpzHFnKEWkZmOJCG48QtQqsV/1DVsFzC0QCdrM8j1dKaJVC6qDCUptrE/jzvrSHKbXGrpufi+uLWUfnNjhIOJliL6hxD09PVhfJ6p84AA5wceOsQ0ZgvjCCy8CALxSLisUCp2x9eJFOmceOcLPmDrPhpSePNbGfkgTPqiap0llzgz1u3SRmdjpG+QQHz16tOOSHg7zM+ZX8dnPfhYAsHcva2GuXWOGaH1tC3/1ZfKjs8o2TCqrlt9m/z24n+P5Hn32q1+nss/hE0fQlF53TXVAM3O8tkPKMKysbuhZ8H4SqR6cO8dnYNz8feIvWx3akSOcK8+8ynk3EAyjosxBVlmAAwc4Z00r+x6R4tvcJWZTuruTne9pt8Yc41g78TaEVGz+9W/9JgAgU+Rc0zM4imW5Q3cPjwMANuWZMDbBbI254W7lOc5f+fb3OyqJT0hR54DqZFYXxV9Xpmhqie277Q12PI327lWN4gr7lPVhc8H1+9k3Wq0WPKpRM4Q9oOIr+2nvtTqAar2JrFR3BNJ3xobO+qqL50lLAc7UFz0eTydDIHGpji/BmrJZbungN118X73RQsilrLSU8TQ9ISzX6AtX2QeSQ8xwR+Ix+JVpnpIr9f/5738XAHDfffcDAAYGOU4MyT9keTONlQ1eQ36b34/P/+OR/du62C+sc6F46Syts/2aaI8c2o306iwA4BUViuzbx4b153/0HwAAuVUuTPplY//s8y8jpOLAVlnSTH3jAICrBUk2rXIRk+jhl/e3X/4iPvYxUm4+9P4HbjneH/zenwEAPKJOXDjzMgBge7uEYyeY1vqMZKX+zb/h4FnYZmO/fI5fbqXAh/+hRx7CY9/nwAeX0XX4M5+T9J5SMA2Z7Hig1NH8dTz2TabUhtQYGipuQosLoBdeYNGLmU2cPn0aRw5zYL8xz86UkGNSQXSh3eNsLJYG2jM+2uls1rmiZtyiVFyXinjCUS5UMt2JzmTnbvO6vS71Aid2PLbBybWqBUVVG6xi2QNPjAvKVpTpy3APF9KeaJc+y+/fHVMheKCNGth+Fje02BY9SOMZKqIDHFRB+ksvPo03L3NhYhJqYUm4rmpgjEpGc0umUb5YEOuiHHhdPP6IDEf8Pt7P6rIKwVV063b70DvC42yXOHANj/P+6mA/OXSMC4w33uTYkejh9WQLq/AG+Vy6RFHIZHhtHm1m6yqmNLer3GoGQwN8biG148XLklhr81j1tmRO6zxGOpPumCYZ7antYf/be4BjVTwl2dAG3xfpTeG9H2XfaUp29K6foPyjEzsbvb0m7MDvzybutbWNzji5qtS40SCPH2eR6C5RrH54ss9pnO6RtKwZHdri+4zmsIbawtS1ixhTOz9xjLRHo6Ns53VcSRVGZD517fKlTmGxpfxjsa5b/v7oRz+q10V/cQMNFeo3G/aTfWddRbEmafvUU08BAB5QIe9aeh1FLdy+/OUvAwCOaONTFShk5/e4ea3r68vYr83D/Mwsn9810aK6uWh6/SwLc21uC4Uine/Bvpd0hv+LqsByu8Sx6O57TvFZXL8Kf5DzUEZg09guhyL6dkVI7amd5Zg/oeJxT6QLuYrkHksEXhLdnFuCAkAiTY7reW0QovEk4OL49gv/4NMAgKe+T5GUmARJ5hfZNuHh/FFrtNCvBaxXC3T7aVSdiopSg2EBii5Pp5C99RbqSpdMS+3/VtA6PDzckalutuq3/M8oP5WqqEE65nax3PnbpDbdovMZbdWluaVQ1DpORnoutxc1yWPaBqCgtu7XRJsc5LrO2+Lcc31zAyfu56L+xjyBo9/4F6Siv/wy16EtjR1ZUa+TA30oakNjEin54o8Xf3BoPE444YQTTjjhhBNOOPEOjduK7IeFTEYkj1SSiU/I60I5S/TjPfezSGPqBtOW9ys99I1vfIOfkcHCX/ynP8UxFZJEJEFU0jH6u5mWWVxiNmBzg4hed7IHFyV3dOUC04fvuoeGWJ/4CBH/iFDsmRl+9tLFKxhW4VFundd76iRpQ3/yp18EAERljpDe5P/fc3ocLhfpQogQrWg2uQseHiSiaMYOXTIOee7ZpwAALk8Lq8s898LMdV1TSJ8hOmJGWb3dRGFmblzFX36F2YaJfUyXhiPcJUeElrRU6GHFYu52HZUKd5xrq0StDOm/4w6mdOckLzUySsRqoCeJhRmmoBKSqWtawbETOx6zOdHH1L5CCStCGkAoTvTOEyG61gqzLTRUYFe1wlIVG/VEIuiWlGp2lW1gYZop/JifSMSo5MpuzLDvvef0h/C1v2G7qjbYXgP63q0odUAFdg0Vw4ciYURSogqoGCsnJDKqFGxXvwr0B2TKEwig1uJn6nW+16Vshv3tVdo5poJdj5f/d7lcnQKroFK7XRn2YUsLW2q3LASngXaHLtEfDehps3+YfGJlm59d32LfuDFbR4HdA1mCOTh4J78DozRFJcEZ0jFDAQ/8krwNqajSpzHQiZ2NUpFfTkuZGkOm3R6gJucoK9SzaAq1W1jkeGvUnZ6eHixJsjiVMhlAtsM33ySl5YMfoPzyjWlSHJuNKo4dY0ZsU4V/gzI4LGjc7k6xj24o/V6r1ZBSYWEgwOud1Zg7pnT9F79I0QczPzpx7CRqVaLzA/1EvleW2I/7VTT87NOkuH3687/IY87x/vx+PzYyvDbLnF++TNpAXSjh8eOcU4cGef52G/Cqr6e62I+CPj4ToztE40R8PUI/Dx0+jLlZPr/F5dXOMwWASEx0Q6Gacz90bfPzswCAI0eYGTFJRCd2PjZzHMRcGhsb6i+jo5NItThW5cr83kMxrqc2JNLglUGoT5nhWrGIuXl+31sbEnRQJszdlkiCTLBifaJHdqU6CLvR3hoNtomWODMm1+rV+q5YLHYK5g3Btz5rmS8TPLHsQK1W6dDBGja2l2VS2Wze8hmTvbW+5na7O+/xBniNybgZ780CAHq1NjTTuHAw1BGzMCHMsOaHkKQ9G1p3BTUXHD5xV0dk5lOfI33vTRmptlSYW9X8N6Rs2tT0DE7cxb569nUKrNT/K1OLg+w74YQTTjjhhBNOOOHEOzRuK7K/LkMm22k1a9x9RUPhzo7NeOMWIyPkNxnCYrv9aDSOkRGiD+feINpyxyGi2mdfJp/S0ItihUjI1PR13CWpzdI29zn/8feIzu/by+KtZpNcyVUZTF18cw7DQzzud7/NegKTHhvsJ3+zr4eFxqkuXs+evceQ3/gjAMDwPr6nrCKKpWmipkEh+vv2kje6tU4u2NjoILY2iYa0hVhWStxx2m6yUFCBzBY/E+2KdwxhIF7aC8+Q1287YNsdG5cy4Pd3TCVsZ33kMO8zou/gkArarsm+fHJyEkEhUFWhtoO9lIpzYufDlWKhWkDIWaJb5j/xAbh8RFuq4sVXW/x+q2wyqCv7E1Jlaw11LC6xTQeEou/Zy++7rILTcxeY9do/wUzOlallnDxF05VLKhwy87ZlmdJFk2z7bhmvubwe9ETFSVRFlF91MGYKZwhORVzGQrncec3vl1GI8ewVhQLRUUNAzRnc6/UiJ26023NrYdfaBseKeIxjh1eITk9PBPUS+1CkbcX3PF6xqSyXpNCaKrYa3hXEiKzM4+Jxj03w3mu6mJoM5owj2vIFENLzisggzTIWTuxs3Hk37eVfeYUc12bL6o7iyBfYVq9PETWX6mDHRKevR5x3FYgWcjm85z3vAQA8/sR3eTwhfPfcw6zu448/DgDYPU5kb2igD9Wy2iGUMZib4XsmxwHQFAgA/Jr/tre3kRU/3eflRU2q7507R2TPEMuAsuFPP/19oMV+VSmzrT74bmbDn3v+VV3jXQCA//RnzAp87OOUW371jTM4doJZ2wff9xAAoNFgnxga4Vzg0pLACmz7unvwd9+kDPav/5Nf5vVrbPnt36aB0vvezyxHep3P+Zkf/D32SOL6Xl1LIHTrvN5Wrdf8wiwAoFgs4IRkIC3rXRFa6sTORzSpGhdlO+v63t3+MIJe9ouysOmuBPtHTXUXAb8yOkLVK/4AWi2tVbSmS8lkqreXc5dbUtHRJDNZPQPDuHGDgiO2/rAaFyuKtf5p/cbr9XZQeFvP2Gftbwtb5wBujI5aTY4MqirNW87XMvlzFa9XVDxea5Y68tcmFWu8/qhqW1waTDzqn7VGvfOZsLIBcWUDzCQzo7nOJ9ZFqr8f01NcF548zsLmZdXVZa+zZi6uzNi1aY4p45MT+Oo32C//wT9kncSLL76IHxcOsu+EE0444YQTTjjhhBPv0LityH6jqR2UkMVGi3uNQCSO/QepCpDZIjowsYco2vUZSjeZsdDzL5EL//Jrl/Dpz/8qAKDt5k7z3geIQi4vcwd3+pEPAgCWJBf5rtPv6yDdu/YQebhwgZzLtFQKHn4/ZS9NsWErV8IvfJo8qstSJrkxNwsAOPPGeQDAox8mAh6XFN9nP/8rCGg3bLvHgNCcblVi33cvEaIlSX2ur1E60+d2IbvFXZ0h+KaW0yWzIsuMRLRjzGSyHTMWQ+uXhZj4Al4dQ/KEQluzhQxuXGOGYlUc0p/9xM8AuMlt3V4k6jQyzL/Ta+vYP7n7lvsaknmTEzsf/fuZhQqJsx8MEyVuu8OoyPSpUmcfqoJ/N92mXMAflbIUl/w+JFNEc8pbrPhfWZbJjwzrervJAW65ZBs+cwUnJWubGlDdi0y1Gj7+DHXxM00/z7O8vIi6UJ5WQ6o1QkU9/2973xkk2Xldd97rHKZ7Znpy3p3NGVgAC4CACIIEAUiUKUsUYNGiTdquMl1WcMmWVWXZoqNo2Sr5hygnWTKVSEKkRIuSCEAkkROBxWITNu9smLSTQ+f0nn/cc7+eHmAhWzW7P0bfqdqa7Zl+/UJ/8dxzz3XlM6Jk79X6NRRIoFgSNi9E5t5jhCpCTWm5KJ+lbiElRhLgA0OD2+RYOjP4DvNkfOaV1MjOhMv8zATCKUYSXTlPmixSt/FYo+NEUViYXKWKzp4h3rN8D0kWVDEuDkWJVFSoT/XcAHxXmK0KbVOrZfVOsNhInGGBxfFJiYqq400qlUI7x+UqrYQLeeqJySBqMS112tk+Oopn/+JpAI2x8OJ5ec9RRg56+4Rpi9PO9dKF8yaS3N0rxwSpg/7qU+J8s8CcLmX0HnzwQSToVvLuuzIPzdECOp2W36t2X3MIIuEgHnrwIQDAW2+KVvfkieO8H2m7Z8/IvLR1VI59863XAACZjg5UyeQff0fm0TyfRZFt9+pV0f9r5Pb++x9ED3PDJsckutfdKc/zn/3s5wEAMeYLXSXreGDvLhNFrDJC1kq3lOEtMn/UmMOmEb1du3bhnePH+FzEmeQEiz1abDxau4XtLs/J9xR1ya5XfHjUuwc5Bnp0l4nHyeQXmZ/FaI0bCiKkkQEWptJij7Eko9KMuqbbG8WvdD2j7LyqO3S9o2y9svnJZNLo+TVfRN+rkTft0/r3YDCI1VyW90EHH1c+VzX6ATrdqN2sRhSCoZApDqbQ6N/WbdKO1eFLoxzT09PoG5C12BTVGpoDoDboVUY/Ioz6RuMJOMxnqxnjIZnPk7qOLNBBiIUBy1UPv/WbXwYAFFgs7CzHqJvBMvsWFhYWFhYWFhYWmxS3ldnftudOAMDl67I7mgjKbmzo++8gwlL0oAY52S47wlePyu7ejcnrr3xTijZE0p34P8++CAA4e4a6Ju5Wv/qt7wIAjnxUWPp/9StfBAD8k3/606YYz9/4xA8DAJ5/81UAwH//jf8KAPjCF/41AODhhx8GAJy6eBolV3ZVv/XV3wYA/NiTT8g1tLEQypI4e5y4IDrLrsEMcgUWYyCRx+rOOHLkXnkWLKk8Q//Z9rTs4LIry9i3T6IcJ08KQ1Ng9vb0lDAn7SxTPjcvzHs8lkQqJcfv2ysRi2NvS/Gs1RXZmeZvyO4vwt1sKBJGgZ77WbJJz9EbV3fcr74s5ZyvszCGV/NNIYpf+qVfAgD88R9LgReLjUcoI8ycssxVMtZe3UWVfvAeNYOqowxG6UzF3b7L4lFhuABZNp/uJME6j2VkTFmZmaTmkvkAACAASURBVDmJ2jzwkR/G8y9KGxgc2cbPl8jR6HZ5HSbbEyJb0d1/j9HjRiOaH8L6AHTfmhiXaNbcjLT97Ooy0jFhLLRGm8p1tS4ESVnQWhzlglxrrVbD1cvCOIIFTlJpiTrs2i36RzfA+hHUOi8t5QF1WfDkOVVCLKjEPIIqO+wSfdQXlquYqwmL40xJn9q+jTkCLJ4SZ25AJOHwckKmPkGF91EofbAXssVfDRpd2bOXEWIWfHr7nRPGZ3+gTxh31cFrfkidTjs6tpUKBXTS9eLMackH20bdvUZQtV9tGZbP7O5sRYG1U65dlXE6vyrf9ciQHLN9VPpMmozewMAAqnQEevxxmavGx4XB7+QYr44lrbyH+dl5XBuT/rNzF2tPnJD5LxYXN6gA+5+68uzcLXPC8uoStu6QOfLyRekzKyvNjjf33iWRvMuX5R5mJsfRnpLxYfyS5PQ8/7Qc+8STnwEAnB8T7fW5UxItuOPw/QjQFWmGRSW1PouiNSN9pYNRl2NH30YHIwYvvSDzukZKLDYehQrHwqx8T9t3SoTWCcZQZ8G1FvrQa82HKNtXnKy5Q8e3QCBg8vy0EKcy1R7Z8raOFn6GsOaVWhmd3R38nYyjiRb5WyYj7UDdB1WdADQiQalW+byWdLLpvnzWCAqFguan+ui3JOnUU2GBSCon6syxcRgdd1xVQ0RQZBQjRDc1XYuFGFGIrIswRONx8/89O0Sdks/xGM6DJUYfcqzTND05hRIja6+/JpFDGmBhcED6q0YbNLLg+w7+23+XIrWf/7xE2BKx5mexHpbZt7CwsLCwsLCwsNikuK3MfmfvCIBG1cF77hFf4kNHHkC1IAzDjp3CeJ+mM4gTvwoAOHNFdIdtHaJ5z6OEY2dFVz89LUzbv/i3vwoA2E6XjOffEKb9H/9zKQn9N5/4myh6suPctl9cAu68Xypa/q8/kKq1b7wj2snX3pafk5OT+PRnZef06KPCvrx69ATfI8z73/lHPyvvXRANfCTSgnJetmbd9NUv0amhK9PNaxaWsKdH7qfcIrvmy8UKzlF/qhqzw3dKlcFX35Bd3+CQMDrnLwqjMjCQxNFjck0PPiDMzMKc5ACoDm6UrJLq4GZuzGGOuQExVvfs7RXP9B7q8A8cEKegiQlhZ0qVIjJtwvp3tAs7laVzgsXGoxaRZwyHLL7P8vGOK/8ABPm3ABmUIJn+aEgYkDj1wa5fwDLdMgJkCwe7haVHSRgOzVPJ0OVqbqGAZKu8p7VjBACQSEl79V360wek7UR43lA0ZvI5lllOPRiSNhhy6FwyIMzN1lFxUGmLO5gdl/ZbLQuLtBASJlAdTgKsougyuhGPybPwfaC9VZjTs+ekP7x7SljJWl2eXyQirGhv3wivJ4NEqzBRbfQOL9eFWSnSsSFIR4UQWZiM46KjU1jcMGsZzM9K/3EDmksjP4OOfk9B+L58L476Q4eEkbLYWPicymrMF9HKljOz84gx+lSmFrktLWPtIJ3erlySeiZtZAvf/P7ruOtOiQr190qbTdEfPklHjeusN1KvCPu4b/9uLDD3Ccyb0eqXR46wSux5OSbLKtFDQ0PYu1/ccWoVyXlZZq2YPkYhSnQO+c53nwUAnDx+yri01arSpg7sE+b+OGvHaFQjzftc4mfu3b8HKY71Z87JtbawSnp+VcaAi+elOu7SgszHW7eMYu8OmU+TIekj6ZiwpzMTMl5U8jLW/ORPfAoAUCgDNKZCKytRj4/L2JNnPouyxT6f1YEDh/DWW/K8du0WRnR1VeZTi41HhXNJjhHTUEzaSrlSRYmWbinVkTNqG2uV1xG6zFSp5fd93+ShOHRqStIfPhaV9tVFFr9QkO8/n88bh0Vl61Vnr7p81d+rHr5YLBr/fF3HuOSrfbLz6uLm0t2qWqujnfVPeLkIBtXRTSvcSn/UOkN6PcFgEL39fU3X1s01kmr1tRZAnRGF0UQc2RVpt4scDzQ6XWeuZYDXlmdkoVQoY5iOQeqwuG2brNe+9a1vAWhEBdvoZlSv1xGOyjO+xHHlJ5/82/ggWGbfwsLCwsLCwsLCYpPitjL7iVbR0tZrsoOanpcd0J//xQvwa8IauNQmtbOC30Mf/wQAoHVAHG/OXLwKQNiDGzOyc+oflQhBnZrkMNnOkxdE//jqSWEr/vOvfxklMj77nxV94WsvS8SgvZWuGb7sQLtY/bDudmMpJzvZL/7qlwEAWTqBqLPPo4+LdhGO7LqqCCOekl3cvDowBDRLnBXlluTex6+Lbvk6dY+O45js9GxWznP1yiSfm+qz5TO6OoV1HRwaNXrRPlY03TIiz08r5g5xhzo7K8/MhYc4q7bddZdEOc6dE+3n2bMS1Zjn833s4x8DIN7SH3tYHI/+lB6vWhnUYuMRpPuOQ/24y0iP47sI1akv5Hs1guOT2YiT+fCrZONScfQkhD24QVeNMUaQMgn5DnduF0ZtsSafdfz4SXzkYz8EAKiRoY61SL+4NiFtI5ES5qZapf9xNQqX7DUJVbgO+2XA4zULO1Pntc3OzyK3Su/jVbIsOVbf5WvQD39lUfpTw185hKvTEnliEAAH9gmTeteRhwAA7e3UozrS3vPlOpbp0LDqyVjk0lki3kNPZOqqHeq6c6WycbJStrVnQNxbHD4bj64/Ht3GPC8CmEqu1KVGbQXdW4GYccOQ8bSNlWkP332PiW5NTUru0cXzMs6dPiXf2/49woyrZv8Tn/gE/uSbfwQA2Me/zUxJpFRrw9So83eZ7/Kd73zHXIuyjjvIzp2m7r+N7k3bd0m7mZmZwblzMjdpVFWdbsbGhK27m/UDSHLijddexxM/9imeR+7r+DFh9FXPG6c7zuIqK/kOyGePj1/D398nntwXL8l5V7PC4Eepi9a5+ZN/Q5zshgeGsTQrz7SVUYHokIwBN2YkqvH6K5L3dvDwAwCAkdF9xhEvDHnGLawDM89rpDGfmUMdz8ddd0pOX4FM62BfPyxuDcIJaSMJ5hkVyzI212o+ylxH6dpB1wlh5jXlWPOEhDXqcAzzXaPeP5KR77vMSBuDUKaaOdDQuStbr3OY1hNSZ0FtI67rGvZdj1Eoo6+f4aBRSdfTxkZum+Y4cBkNTyZYk0ZzEbhWSyQS8JnHpg6IFbL+MzMzfH50fGMOGNCIXrXonBKW+9R6ATG+LnqyBkwnkljhenCB/vqry5KrunVEVBwdbXT/ogLk1KlT5lmcZJ2pC+ckQvljn/wE3g+3dbH/3EuS7De6ZQQAcOachCza03Ec2CPJOG+9IwOXFti4dEUGmp5eWahc4SJjYvIqWjjAO/z2NOReo0yhSh+jllYZ+Nu7B7G6JIOb78oX8aGPyGImx0WFJqdOjsvEcPjeg2YQvW9YFkMaVhrne15/TQY7MJTjVRuFGbyy/Iwk5Jivfk3kQjoBaYMPJ9r4lHyTzBdNy3XnmFB55xGxJGsUgwjyWUwjT4vFX/zFfwkAyLJQktrJLTJsqvZoyWQSeYab3nlLknk1ke3NVyVBSht0O60G+zrT+Nrv/x4AYHBQrk0t7iw2HoEQC6WxPfhMiHIRQMhttjqD+Rtfc9BLMKkqEqhjmSFCjsfo6pc+FeZifGJW2sMM7SY/+cTnUAeLqGm4N0ppTFw+pKtLNpWr3JiWq/VGshaz0/NcUFQCcp62lNyXy8VzOTePcpEbAJZoj0dlw+BVm4ttLS/KYNjSIm01HEkgmaD9J6Qv7dori4ZEgrawAel7S1npI+2ZHoAJwTVKjGoBGgRwIx8g6aADf8grIsJJJ0BjgSAXMpqE71C+Ew1qwnPE2A1rCBmB2zrk/rXBCoukzS9KG+9gEZp8qWikA7pBbOWEOTcrC/hYRL5rXVy++NyLRor18ssytj/2iBAevZTXvEPrSn9G2mOqvcuMy0VOwgsLEupvb5dr0UXGNRI8qVQKdUpyLl6WpNsrV14AADz0kBhEvPCinD/NZMlP/fiTAPtNMi0bg04SOVtHZWHgc14YGpWifAEmBo5sHcH/+abMPz/8wzLvqdRHF3YnT8rCobNbiLmVbBbg5uXCdbHlnGAC8tG3xfLzbs5LFy+LrOfVN4/js5/7RwCA770ghSjbKZcLs7iSS5nH2fNCOFTKRbz0iqwPdrGYo5oMWGw8PC58E7Qy1kTxWDwJuM0Lal1s62JVC1bp7+EEEKQ0RSUwWiRO+8IySZqWBBNZo9H3yHX0czWZ++BBIXHDXJRX61VDauqxHuc9HWAjbFe6vI9HI3Ap1+E7EaYLRDCkPym7dJsLkMbicZSpR6tyMzHP+UcTjQucGPWYbDYLl1KmSkGOTXATUWBxV00qbqOMqb+/35ixOCSqdfN/7JjY0aqFbTqrluc95rnNTNE4QitN3gRWxmNhYWFhYWFhYWGxSXFbaaauHmELbszJTqSQEyaxozODY6dlh//OKZEYnL8krLnapOG6sMzDLOqzMj2NyQlhQzzuJgs52fUkk8IsVjTM5EsCX3tsACXuVhMtspsrlGRnVqdV0zKtloLc8S6VivDJwitbH2VoKsUd2vCQMIuzZMJbW5Jmd+oxvKM7w0c+KuHRutnVyk6uWOF5QyGzi2tnEqxGFjK0I9UCKNv3yk503759OLhXog47t8iOMMBS13/8jW8AaCR8VKqy075+ZQzdPZ18brJj/+hHHgQA9PJ7KuTl+6mV5ZhXXn0Jdx8W1nRgQO5Zk8EsNh6xgCbB0uKMfIXjAwHu4pXJd2m3p4m7Ppkauomh7jhwyfLXIvJ9j5MFDZO9TrYLS39gRKQLeSdq7MhAS88S7Tq1uFapIn0uFJbzpdJxVCkFcMiKtLZqFIKhVV/YmWpVj3XRyeIhASFBME1pTpTJVDG1OmOSbJmdO5etwicLOzK6k+cbkfNHWniNct4EI1cVJwyHyU11X/qdx+enFmcmQEJmPkFrUwAIh7Q4C2VVYXmPT7mVTxlP3a8BAYaV9W+wRbVuBWqejInD26QNqz3g5NQ1xILCKnd3STLdVTLQo1uFQR67LHNNmO/z6gEMD8l4WaZtX4ntbewqizzyvZ0Dkry6mF1GiY0mzzG/TsvXJZo1xOMNCQMA5Mr1xvxGnUN7Rtr30aPC9OVZTG5+ViKzW3fsRIZJgc6UjL3H36FFM+0uE2zbVUrU2pLStwr5JXRwDvnOs38h7+G89NhjMi8NDEo0YJrJ50ePvm0MAnT+W12Vz4317QUAjK/I78uMAm7Zvg2rORlbDt8pEft3T8u8XijrvMjilv1yPXOz13HokBhCnLsoEqZ7778PFrcGCSoNNNlWGflisWQY4npd1yjSNpW9VmWDJlC3tKSNZXKV7HWUY6RaSDocZ8uUQBYLObMG2k1r2NkFUW3s2iv98pnvPAMA+MhHPsJrjaCXCfN6jUZOZ6Y7FuJCQ9aqybo12oImEpRDM0q9qAm1SyJPUjvpRFsa7a0yZ07ckLXdHJUhGrGI87y9vRJlW1haRSBCO07OC3O0EO0YlAhcgcoTn8/o9MVzaOmQ9WaNUfZSXcad/TQKOHFMZD3D990PAJgan0KK0T5l7IOBD5aIWmbfwsLCwsLCwsLCYpPitjL7DrdfqVbZxWjhkHfPXcToFmEUVpZFe/ntZ54DANx3jyQo7dnNZDjuNu/Yvw97trEw1bRoL48fEx3l9HVJdu0dls/ctVOOfemNt7HIXdYFX96TStFCMl9suqZFarNqNQ8p2q1VmDjk1WVnphrerbz2VkYULl+6YBJk57hbzRVkV/knX5PS6Q994pMAgCBZmBlanbWkWs0OepFFFzyy/9eYfNaRFjbk+pQk7vqOg3dPy84vxd3k7m1yTXfdLUW8VmmRecehwwCAew7fiXRKdrC5VflbKsGy1dwtt8SoYUsIY3PvXYfQ3SUs0fSEPD+r2b91iJDZ136jcOGb3wVMQRBl+IUZ0FLmNUaMioUcCmRVKixcF2+TKJDqmzPd8rpcj/I8IVNa3JzbV35Ak1CZdGtKnTsIMFJQC5Du0QRj/t7jZ7i07XQDUazeYPGsnFxvG21BK1XpAw4/q8wEyZUFGSc6B4YQoza/tUtyENyEtFHV8Ht8Vj6TZWu+YxjNcDTFS2RkRBOd9YbXvl73N41UvO97+YQ8kzCG5vdabCgmbwjjHmXyWxvHyEymDfEQI73UpQdoKZhkFCe3LHPC88+9yM8IYnSLzAvJhM5VtF0Ny7GhRYm23piXY1fyeRSZIV5iJLTKpMcgI8Eeo62K8vIKBoZHAABzczIGJ5IsLheUdl4h43/oDjFRePvESdxYlPNUqUKemBbWUSPaQV7jCi0355fkfWkvbXLWYnxvgBrnP/+2sKjZvFy7Fimr+Y0cgGBcnmm6W+ZdLd5VZ7Q4xmh5d28P8owKHzsq+WBbt0qkZG+/RNnfPSfs/dKSMP3FWgAx5i9oMa0JFhiz2HjouK1QpjwQCBjtvMkn5DpHk1RVT66Rza6uLrNe6uyQsXd2ZrrpPeWSHJvnOmtgYMCc89QpyRMB54eD+0Srr/lS0zOiBKlopUUA7Snpl8puR9mndQyG6tf9RuKs47I/cj2lU5uJrjGfSguCua6LPDX5qRbpl920Stc8oKmpqaZnVCwU0EtzlzlGA/qYIH/unOSjehwPhodlvirMzKBYkefSxrWxqjlaeZ/bdkqf2LpN8nLS6TYTzVhhn9YiZDeDZfYtLCwsLCwsLCwsNiluK7NvLI/4Ok+NVCQSw+kzYgV2x0Epdw5fNFD9LDW+RF1VPis/twwPIcldj+4eFeois/+QsCF/9Mx35Q/hOFJxZRNl99bRIefJrV6VY1neW8sjV0tlLHPXFSK7mGaBlWnu6jwyRnWyqNFYAnEyJ8vvnuZVyaPuozuOFg2L0pKsvUM00DM3powFnLrj1GrcXTJHYJY60igLC83NzaGUl91dd1rOO0vW/43vvwUACHBXO0rG/8EH7kOa1zgyrKWy5f6uj8uzSLHgSoRZ6929AwiyCMrcvJwv09EDi1uDcEx14mSHlX1xGvywavb1dw5FkoZ9Zg+PJ1xTDCpBJxs9RpmNBNnQXFHdf4INzT7h+M19zV/HXAeDLpj+At9nwS9lAKlDVL19hdr+UrmOln6JhJVz0sbPnZf+0dUt/TMal7Z+7bI4gfi0HZwdn8KP/Jg4l4QiwiohIH2qSqtavQ6Hxzi+B4eMTSKmjhKMUNDVwZRQ5+ua7xkmRf+mrynRf9/XN/ubxcZCu4SykspyBeGY3xUKMo7HYhG+lqjR0JC0veyKjGlt6RZMcPy8PiERg65LEj3KkLlUZnwxRyvJet3ontfa8AGNiEK5XGz6eyQUxtG3xG1DddCXLkju2sjgSNP9XLsm+Wnd3b2YZBQYzGcJ0c4wRJZe2XotJqaa+0KpjA4W5SlTv1wly3ieOvkuRtQ8RggzHV2YX5Y5V+e5K1ekD5bogNRO+92oRt/yOezaLgzk/v3C0moey+nTYus8vFXmwWxB5sxAxUGa7iSaFxGL26jxrYKy5i7nc33tOI3+ElJGX13I+HvtE8YZB77R7+uaZZXrND1GGXhVArzyyit44sknATRUFMdPSe6Juv4cvlvWb1/5ylcAAHceusNEF/RzVQWhE54piMW/12qeccdxuAbz6ZqoOVbq0hWO0UaTv/cARMj2rzLCXKa6o1qSvgztR/z9/NQkyrz3Vt7zPAtwbRmUtWyW1qUe14sH9u0x60G9d3WFrFaabUk1ClIpVc179LtrobPWzWCZfQsLCwsLCwsLC4tNitvK7Fe4S5mfF53eFmrdH374YcNa/97vfBkA0EUG5bU3pIS2si4pMuGVSgWdGXlPa1qYyaGtwiaEo/KehSXZMf7g44/KZ715FEW672iU4dWXXwAAPPDAD8i10Q9WowNDg4Nmt5VhUZSdO0R/+P3XpCDXPJ13HNJLPV1duDEtjE8yI4x9LCnXqDvR+x4QLf0rr74u90Of/Hq9jtZW0Yfl88KcqB5s4pr4HLcktZCRPM/hwQEEqEMbuzrBa5XzTZwVpqizS3TZx46fAAA8970XMLpVNGOH7xD25cB+yYpfpkuLlpNPUstfqzbKuV+8TM/lKXnvj/4d65yw0TA+xsRaG13jzEPK2LjyUMdbrUh7CtLzPRpPm8iU6jXVyUBZgwo1xhHjPOPiPXzAOma/cUnKltQRpDZei/6oVlKZzSIdG9Q9K5/NIaJ+957c890ffgwAMMOCPqtZaXc9W8SpQbXMjx95ANGYsP+eE+dnkP1hnQ1Qc+yueZ7hqIwDYd5OwLA9zap6U6zM99/zN4203OyYv+xvFhsHfa6qcV1ekHG8Wq+hFpTvWlnBECNZk5PC3n/nWdGrq964Uioglxf2TVn/JFkzX6PTZA5LdNbwPM8whRoN0wJCFfbFIt+rEa5oOIJPfepTTdeSpY+2RiYOHqQbxwkZt51QGEWy4clWae9RznfLKzJPqYNQJ513dM5ZXV2F2yV9cm5axm3VScfo1NXZLWP+/KK4/+QLZVRYKya3IhHnQTKUtYo8x9yyvLdEF7lYyMXEhMxDWgRPCzfqnHXh/CU+N/AZVcw9bqGrlubnWNw6qCpirde9cRlTxxuy5fpzeVnWYu2MxCwvL5u5StutrmFMISlGqXu66BYYDGJsTFyxVvO59z1G6xh9+tOfBgBMTUw2eeADjbatxe78muau6djvmPwTjapWKtJOy1RMaGQsFmFOF59NNl9AqV7kuaU9L96Q+ajMvpyiU2Oc6od0PAK/VtYTAQA6+ZzOnBf1ij7fEpl9r7bV5Klcviy5kDpHV+hop5GRAmt5hNwQHP2egmq5hw+EZfYtLCwsLCwsLCwsNiluK7Ofoib40ceEtXvhhRcAAM+/9KLZ8ff2i358jhnYulMs5kUzFWQ1tQtj17BKZrC7U1jrdu4MO5gNfX1C2JLxs7Kj+vjDD+GXv/grAIA9+yU3oCsjx1y+ID7Af+vTPwkAqNJT+Kk//ENUuIsrcQdapfexas1Uh6bazLMXzqONDJPuVoP0s61yd/zKK68AAB7/QfE3/taf/TkA8fnWzysXZbeY5TPQSMj4ddFvqtb68uXLSKu+njvaSTI3xZKc7+Il0Vlq+ffllSVcuiI75xdfkiqNEVZa3LNLIhePfEy00IOs0Dg3cwMlVjWMMadhfEp2vBYbD6NHXAfHcRrMvhIY5m/CbCTp9ataZt/3Talvn+x8wGX+isl5IVOgzgZr0GCk9bx02DFENfXxcExeR0DNl0k5eLwfrXKYSjDa5bqGxdm9W5j7xRVhC6NJ5oQEJfJXoOPJfUfEpSvd2oFCUfMWeM/0QPeDDe0lAPiBBovl0rHEqbBirm+eYNP9fRD0ua1l/9f/VIbGMvq3FlnmdFU7RJOu318kEjEa+TLrkySi0k58OnWoG5VWtpybm0eIIZ92zi1Zjv0l6v4nT4jWPkHGPxSNmPFf3TbCcWH9gswHCVSaIwxLK6v43d//AwDAz/zUTwMAvvUtmQe0jsn5i8L0LTBSEU2kkWC/cRixCkdl7A9xrHeYqBNjVXZ1VYEbxcyczCU1Rh9iUbnnVDu907NFPgN5nm6ohFbqsFPtwqbq/DTOiHaRDivD9EDv7mxHIir3GOZYcoPOJKWiPAOH3udllvNeXV1t/I3fnTLIFhuPcknWWzrHaIVy1wnCUac1Oh/WyC7rWBZje8u0S1+rlGsoFph/Rf95jW4pS1+gy1OebmtTU1O4dEmiO5/9+3+v6b2aL/Pss98BAHzyk+JcWCiVGxFgSjM8w2br2M48sQAr6bpBVNj/fTOm67jdXB+lxvwsrYS9vLhoGPyVeVFqBBiVm+UaLBLSfCC5dvieiS6OTcqcNpeU1+mU9Ns5Rs362ccHB/rw7WeeBtBY4y2vZLEWmgOq30E0GkUwQpessFbsLeODYJl9CwsLCwsLCwsLi02K28rs72Nm/p8/LRpJzYLO5vLo6REGb5KZ3h53k0MDdAdoE9YiQl3VSnbV7ErVmz9GJuXgQTlPO1me4S3iC/y///dv49D+fQCAnj5hq2P0Ze7qkvO//vJLABp6/4GeHrPj1F2jZp6nqaPSqpu668rn84adUBbnysWLcgxZpAI1+k/Rd/+JJ39Czv/9N0w0YHxcdo+6u9Qdpz43/VkpRWB2tnwmBe4MW9pEzxwgIzo5I/dVrxYxPyeMzLZRVrCbkR31qTPCJo2NiS5///69fN8WPPLIRwEAg33yvbz+uuQcWGw8wtQFq9Yw8D6ss2oT38u0s/oh2Wz4Pmrqd6/HUsMfJEPok4F310QU1rPVhv1XZyCNKSh76TT4A/08ZU7CZFva0+1NPwHgzsMfAtBgd7aMyPlUs9/aJsxjO6uHLtGH2KsGEXLUN1mdtljB0NOKvWSo+NNxHbgMebh0NHEM/9+Mtc96Pdu/3gVs/bMSHff652dxK9DNdqFjsPnpOFialzFvaUF+Dg+I5ryrV8Z8dRXRPK1yuYTdrCKtOV4VMpKqVy9p3gupxXgsaRxClAmNMppralGwnUbJtNdrHhz2p1/7tf8CAPj5n/95AMD//B//CwCwsCAsoLrzhONJJFgvY2pG/haOkHHvEwbRJ/tI23rUyPiFI3EsLknEvIV9T9u/D+mb8wvC1ifoKx4MR5BukznLg7KqMhfr/Sob2cech6uXLmGFeV8ZjQpwrjx3QdjcPVwLqKNI2XPhhuQ+NH8hwTw3i41HIwqlbXPNnMJxTSNi612m9LV+T5VKxfxf1Qaqx9e1kbbj69dFCbBz504MbRkBADz77LMAgJGtW5quSduVOlElEgnT5vR6taaEU2/+vU5L9VodVe0IOtYz2hQ20WiBrq8W5iQ3ZW5uDmGNQHHNV2aEr5yTqBMLyqOUk/VWwGlUxm2hLd0Cdf5XLsl7VL2yOCvjzezMDHq6Opue38oqIwXE0pJE9jRyUqlUUGUeRChGl73QBy/nb+tiX62Nikwg8viwe/sHjKXQ8KA80K82+QAAH4FJREFUCJ9f+OKSPKBEUm4sTxuwRCwGj4PnIh9MgLIe993zAIAoC1Z1U6oz1NeLPi7yM52SOFuhhdLk9asAgEsspT4wJMmrsVAQOYZ7g2wkRRbI0sW2NuwErc6SyaQJi62wEfQy0UulPyF+MVvYwP/wqa8CAP7uZz9rOtV5yo92bpfkjXfefAMAcMeRIwCAi9xAHDy43zTQOENs1YoulmRw38MS1BqG9lHDFDdWdEJErsgNFUO7GiE7e0E625tHT2DyhnTa0VFJhu7r64XFrUGYIUKoPZqpF+K8d7ENfau81kEhqEmJgSC8ULO9pH6GJslVGVLXDarvvN9iH03Huub8arO5NjlVB15NHlYLNw7U3FQE3BDmp2Xw7GBhr1JO+slQ7yg/V9pmfob2iC1dPAlQ50APtdjkeTT5UK0y6+zHddTNrsjnZt9vfoxr7uHmi/T/F6nPX+W9Fv//2DoyAqAh/Sj4Mu7m80WTGK5jct2jBSx/P0Upon5HQyPDGKDls5GSsO2scI7J0Cq5wAJabZlWs2nQkH6UUkcla7Q9higjGsp0NmQPPPfVazImJ5Oppp9qFb2aKyDEQnCaiKtFwrSt5vNyPi3kWKnouB5FmXa0bkmTLnn+gPyEK9ecbu3gZzrIsbjjpTEhgXbtlD6ZzcozvsF5JMfiQdGgix4WH+rKkNzis+lnUS21HP3wRz8OAIjH67hMc4m9B2QjUKu//wbcYgOgBQ41aZVFBpUQARqLbl2P6GsjbeHaxnVdY0UbiUvbXlbrTS7Gfc5huv750pe+hMd+SCTMmpyqC92nnxZJi0qcdXORyWQaCbpc6LpKcgaazRL0Z91ryNpqa0gYAKjxvrRYl7bnZZICpVwWSzRJWaH0prAq48HOLbKe62iTzWoPLdzHr10x68Kjx8RK9K2XpVjfx3/wcbn2hFz7dVq3p9rasMpz66JeN+FJPpMSLT7TTLpfW2czTMmcX/7g/mJlPBYWFhYWFhYWFhabFLeV2dcSwCqZuXRJbCGXl5dNuGdiSsos79jGohvcSVVplzTHXZfb2YVsXkKFIYZ2DhwQiY6Wc+7KyG7r0D6x8vqhxx/D0WPvAADuvVesL7/0pf8KoFEsYecOOe/MrHx2rVJGkTu/ICUKyvZoEpWGvdZaJZZZtEcTj5Xl1LBlnElOF89JFGKEzNTv/s6X0UH5UUtKGPYQGamf+rmfA9CwYdPElTfffAP5rDAn505LSea7DovVWZmJxnMLkpiVoFXUufMXcdeddwAAplhILMbQrdpYaRKjFjtpz3TDDcr39NTX/wQAMDwiTM3n/t4/hMXGIhRuZoOdNZId17D8ZDbWMfxJWtSGQkzO83zDIqr0QJPdlfEMMNHHDTVkPVoMx1/n67We2dfX9XoVAfYHjYRp0rBhXepqK0aZj+ejg6ygp8m2bGc+mLTHqGCCEohKjmxtvMXYzqLOayXBoUmALhN1Q3xWnkd2H4DvUlKhwVwmL3s+k5nX3L/+XyVLntdsf6pJacYO1Qne9G8WG4s6xzmvpm27YcnX0yUSHzVwWKYdpEZGte1q1Nd1XZxgpFmLBPXSpECZt+1MJH/3zDkeEzT9SZn2oBa9Yh80Re049mezeRQpr2yj3PLdd8Uo4jOf+QwA4Jln/gJAQzra3dqJ2SU5Rq091XpTmUll/NvJqkeilF/UfUSilAZy6o8wEhzmvKBRPY/9ulKtGCZX2VSd//I0zdAo75379wAAxi6cxxQT7lUiqGxqiM9o2zZhc1VOUq1WsZ+mGQt8xvOc6y02Hso+r5e9VSqV9/zuZtFd/e6y2SwOHDgAoLHGU2iBUI3sKEv/b/79v8O770qBtedffAEAcISKBV3XOGvsQAExO9Eog0YKPLWaZv/XtaA5Nhwykp4ame8y13NFJuznc9K3ckzyL/JaXR9YoNQmyPM4dbnnvdvFxITdCYNk+qfHzqOWl2tppbLkoQ/JfQ33ybp3fFrWuK2MhoSCQRMRqTDqXqo0J0XPLsw3PYt6pWrWnSmuE9dbda+HZfYtLCwsLCwsLCwsNiluK7OvFmfHjx8HAJOU6zgOamTClflShqGudk9kFe5n8avRkS2YmZUd0hXq7E+RFfn+W28DAJJkK75KXWJ3Z7spmX7nnXcCAAYHJdFUd63jE6KjimhyZK2OdItoJFeZ9FqgHk0t3Lxqc8JSqVSCw51nZ6ewSsrw6+5LdaKJuOywZ2fkXjozbcYiLt0uUYBpMu/Pf0/Oe9+HHgDQ2JnW6z5mWahMreC0OJjaSeWWhI3x08Ju9fUO4dg7wl5tG5W8gRx3uJrwNbckOrUY7z/kBvCnT4sdVi/tTceuSGKOxcYjsE6HqKlEUu5bGXUmzLrNDLvRJVKfHHQDiLE4mkK1+sq0e3oanrfmu4BGDgJqaRZo/gwmIyrjEksmDctqLM9ItZsS5wEtcc4TegHkivoeRgWaCnsBlZomlMlnu8pMoW70jSCb62i5cy2U5Skb1Pjp875K6t3G6IAm4RtPU18Tkj24ys4zUlGv8/swicjkTjQfwAnC499qVS12JqxSZ/yDWRiL/z9owUGNnCpLl25JIUkmfZLjaJnmCNrWusm4BWn+cPDgQXzko2I7/I1v/BEAsckEgDgTTWv87h96WN43M3PDRBUuXpJobbJFrqXAcXrLFmHAtTBhb/8gVhm5DrEQnUalnvrDbwAA9u0Ttnuc95fp6MLSivx/iNGGeSYgG6MIXluh0CjUCABd3b0Yn5R5xglwfGDXWWZivNp0tjEqEEskscL5TvXWPd0ShVAjCY06fPObEu0d6u1FJCzXkkw2J2z2UrMfYcE9tRRtae9AIKKRCukjul6w2HjMzcncrontyrjPzMwZxYKy8Zq4qvOQRs00YtXRETOJt4o48xc1KlStMveFY/93v/sctm4V45SdOyQZPkVlwTzbhF6TzmUtLWns3CqMeo5KjCzNWeJci4WTMq7maAcbS8RMkSxVOeiaSO9DzzNLFl/VJNFgACHOq7OTsi782EOy9lri2vOee0VBAa6dOpJxLNEqPcyxP8nI+XUqWdr4zPPMpVkp5OBV5D1lFgXTcnKaV6ARRrVur9bKJjqzvjDazWCZfQsLCwsLCwsLC4tNitvK7D/zjFhuPvCA7I60bPiVK1eMdr2nV1wOppip/MgjjwAwyeM4evQoAOCNV18zO7LOLmEaNJtb3QdUE5whw1LzfFS5U1J9o8tM7Qz1/co0hLjrO3vuAnbskp2nT1ZxeFD0WVMs2tXfL9GBPF0LUqkUFsmK63mUhVd9leocP/zhDwNosCTFYt7kNJw5J5GKkyclq3vnbrkO1UlrtKNcLmPnTslLIJFrrK66O0X/v7jYrOmu1j0cOXIfAODaNSm4dfWa3M8BWm12kDFdZT7A3NwcYlG1e5L7cELNbLHFxkFtvZRRcYytpWMYdyegzDNZbTLSMaPf49/992oulaNXS0BNEVBbQXh+w0bS1CNRuzI6NKhWn2x3tVRqaD0ZMTCl2Js/olG8BcBCVl0IlP1X4T2ZfL/CZyHnVf296wRRKq7wb/w8Z12xK34GnLp5rU5gLgtxeXTyURZUI3H6s1ar3bRA1vrfO+vsUPX4tZ//sbvuhsXGIZlkfhMjwu2twoQFAwFcuSQuMqqVV8ZS9cX79kmuV4YFtMauXsHF3xLry0ssarWXls1avv489f5FFicaGOzDCqOn+/eJm8z4hLiYHT4s3/WNaRlftb0EAgHjTjN+Vd5bop5Y2fLRUckhm54UV7UzZ86hjdHiIqNF3V0yZ07fkPvZxmMu8L59tvWFpRVjOa35OyEW39N2Oc1iltcnJC8sEW9BisXGlPU/e0bmai3kqIuIkWFhascunEeGz5+PC4k4bUHZRxfnpb/XyZzGUh4qHl1e6Lg3OT0Fi1sDjZpMTAgj3868vK6uLtM2dM2i/WaORdS0/Wo/ymQyuHxZ2tqePZK3oX1L11XaL0ulhs4/z+MbRd+kLWgkQY/RXJpoNIqpOWHf1fQtw+suZCUyFa7IvKdjcalSR5nvrfvNqhHQ7S5E1yxl+jUXoZwrokJnrSP3iBIkxCF9cFj6bfaGMPwLM9JWR4cH8cwzkovQz1yhEqNyQRaTrBRkTahFZmfm5pGku1drh/STBUYS1ZDKzEecRyrlInyubzUN7C+zd7bMvoWFhYWFhYWFhcUmxW1l9iNk7d549RU5OXdSIyMjuO9+ZiwPi7/90TffAgA899x3ATQKWKkbQqqttcEYksnL06GjRDeCFJmHErVab795DHcx4/uXf/k/AgB+5Ed+FABw5YowK1qQ66235PyPP/oYvvf88wCAzg7ZqenOT9k63fH20CN8enYG0ZjsVu+7/34AwKGDor1UJ4On/0zKoj/1VSmXvtbNRF0dctz5lqjzrdEhpKtTmP/de2UXnU6nTUGYSFCYmk4y+qdOiPvQIeYonDsn7hFtbW04+e5pAI2de5CayYVF6vu5U0y3sjx7IAiXmrvr4xNNx1psPC5cuSr/cVRc39iba9s3rjhuM7PfaE+qL68bRmb9Z2g/VD/iILWAdW8NO77Ojcej1l11yupdXinXGgw332sKoDjNhU9ULl91fJTYh+sBdbKhXpOMPmplnof5MeZWHMDXYYzOH2iG76pqkz8DHhw+y1pBb6iZ91j/HF3XfU9Bl/Wa0psx/Gt/Z4tr3RqoDr/K3K+tLMiTTqdNjpWyl0uL4myhtVs6u4Xl1HF9cWUZ7WQkR7YJW50j464MuDrHXKMvfjgcbmjm0Si0BQDFohyrXvk6hkYiUczS9a2T+WvKmqtP/QvPv9R0bZFIBGfPSsR3126JwNY5P/T1yvx07ISM65rLktVaL/2DGGfxoyrHhZYW/SljfJR1J1pTwjB2dXVhZEQi2Rcuc57l81xh9PoOPgswot7T04fFWXnGGjWcZK7AMLX8CmVZV1dXsUJ3H82f0ci1xcajzuSQdvrDK5ufz+dN4TgtoqZtL5lqMe8BgK4eRpiKRbRlpG+V1Q++TZQKqq2P03lGcxjbWjPmPVevXgUALC5LhFaZ/iTXfIGQtLNkMoVEguoN9sdKWdpcOiUsudZnyuXks1raImbNVeQ6UAsoxiPN7liqKqmV5ZrnZ/LIrsh6s0CFhkP3rL07ZVy4ekbyHke4rsT4VQQ5X8cYOSjnpb9U2RdqjMKH6DA3PzuDLJ9xF+cMjeyV2ae0XoE+m6DjwmFfDQWtZt/CwsLCwsLCwsLirzVuK7NvWPl0c6Z2MOQazfnXvvYVAMAQS5orE6bsuXH0CAYb2i4yAaoP092P7uC0BPjIzt24SkZ6oFeYk9/9PWHWP/OTn246z913i87SqwOH7xA/+lU6FmgEQc8zvyC7Mt1Bfu5zn8Mk3XYuXpLy4MffebvpfnLM+I4xAqCfVSwWzXsG+yVSUK4KU6SOE3VmbKv38913343nnntO3ltUBlSY1gd+QNyL3n5bch1UqxcOh5Cgt6vuIpX5ypElq2l1SGaCO45jWJdFlkOPxSyzf6twYzX7vr+X9tHM7Ju/6UtPq8aSda55poKsutW4ZASCZPTVcadUW1sFVxnpevNrdaCpK5ut1XfD5j0NS3nNG1Cfe63MSOYIHmpRuYaq26zV1yQUr17m+Vi3glEB33NQp7++g3XRDZPPwGsMaHSihgAzCFrCwi6hqpGQetPP94O5P3f9/TRXmlx7LTd7bbEx0Ejm+fPUk5MVXF5eNJErjZDV6Oy0n7lJOuYHqd1NJJMIkHGO0cte879cfrfL7JutZDQrlRo6OnRek/lB66XMzYl+ORaXMV5Z+0Iub6JbylBqZHt+RpjxK2MScVbv/kOHDmHbDpkrVWusOnjV7N9z12EAwCzdc1azcj1LK1njgFepqT/5e3NTgIYP+9LSEpbI4Le0Jng+af9dzBW4ynyDvdvlumYnp9HaKs9lirkGMfr551iBeH5Jrq2rT/LdOjrTxvVN3VKWVujgYrHhGGcdBP0ONcdP+w/QiKyoNl/Xa/pa28jy8rJxGdS1hLLlN27I99+SlEhR0JE+UK3XGk49rN7rc97R/Bv9fI061Wo1XLki+YWjW4RZ12q0Vy9fBbCmxlKPrHOOHT+NKMeCBbanAteNWnOonREGPV+K7y9kl+CwYu35c2cAAJmU3Fc6JvPHu8dkXTV2XnT6AdfDQz8gOal/9m3JUR0Z6uf5Zcy4PCkqjEROzvvxRx/B+Jz096k5WSPr95Kno5ZibVS5znFM3e+wzilvPSyzb2FhYWFhYWFhYbFJcVuZ/Sef+BQA4Ld/49cBAP/gp38WAPD1rz+FsUvibtDRLuz8FLWFEbIX+VyzZrJcLpkdZktcdlvG8cJrrj4WiWmVwCj2jIiWU735laF54cWXAQA93cLGbKHmc/++gw09PHe6R98Qvf3ug8L460700B2ii5+YmMApVmCcIMPvkZFUBj9KDVaQTHmC+q1YNGKqzuWotZzi+RNJ7lC5Cz99WrSZDz74YRw6dEjeS4egd45KJGGVTI7q0ZaXWC0uX0cXmadKRXaPylS2aK4DNWcrvI7F2QX00oNXPaT1p8XG4+piM7Ov/u5r2eGbMfuxiPrQ8xjfNVp2V/f4ZNCUrURd3QrezwOerIFS6srWh5uryFYqnqleqL70yjyqXF0/QX39a46LGeo16/TCd/jT5btdvXf1zudnOnUX7Pbw1+jr5b74Wi99jZZeo351ukN4VWV9a03vXYv12vyb/d5UeVzzGev1/hYbixCZ7yjH1zT93c+fP2vYvnJFGG51SEuQFVS98cSUsJCO4xgnEjDqxa6BeFLrmAhLOMIIdDQRN5FmrV49N6/zhvTFKvXMqu0PRz1EOO6nUsLyqfOaVtR16fDTRbeO6elp42alnxOgdlcd64KM2DmquWakIZtbQZDzjrKByuhnMjJPROkJrvUKstksCjx+cVnmnYY2mBp+3rdqu2OxOCLMF3DYyVfoLpJilFhdhqL8nhYWl7G4KmNAO+cl9Xu32Hj090m7Nfp75gHCd01umEZjNFdRWf9gQOaHUlG+79Z0u4mO7dghlZFV5TA8JOsojRJoxGdlZcWs34aGJCdE+9wMP+sGHX20nbuuiwzbj47TS+wfmmcQ4polS/VFMt2CAhnwKvO+tJ7QyhJzdFidNkyrHZ/5C+FwGM++JDkzf/sJye0c6Zdn8eYrksd592FZA/Yz7yexYzuWjkue5AP33wsAmLgh48DYdYmmHLlbjgmn5FnUg0GcOSORg5HtUplbcxL0GencovNHpVxEUevVUPkRidJl6Ca4rYv9r3/9KQDAz/zCLzT9PpfLGevJaZYSrtOzKxiU3+eXtSiDfJntAwMm1KEFubToh36pKlmpUBowfWPWJEtpeESTmM5dkM2Ghrd0Qnj1lddN6fI5hlrM53KRoWGmbz/9LAAgEo0hVyw0/U1XOsvL8hnxsAymOnDq/ZZKJZO0pKEwl41l6xYJ8V4eEznPJDdEzz/3AgpMHPn4xz4qfxuX0GpHJ5PSKKHqYqN0XRcTbHx6P3rPOmhrQ9PrSLe3m8lBFzT6uRYbj9XauiRRLlpdx31PAqn+1OVlqcqFrasLXK9RgEvXmw3Nj/yea9hKXvpa0HXXLE51gasJtPJTF7E6/0dCIbhOc4Eqxyz2VV5DqzPzPgdbucmuq8WmXpPfnFRlZEQM+dZrDkK0f3XABEi9GK5+1ltveqg2rNkKJAZq75+8rH0xEAiYY9Yv5tf/fD85j7uu9LvFxuLECbGKHOEYed99Yis8MzONQFDaXXZBxje1fA4zjJ/mAmKRxE8snkSBEgO1ZNXFfRfJjQhlC2sLAKnd5MyMSA3iLJiobTnEQlJqd+j7vilqdOqELPLVBvTNN0Ue8AOUYY7RArS1NW0KMKr0tU4tUF+ffNbpcyIdjURUliBk1Pbdu5BnsSEtOlTgomJkZAQAMMn5Tzcm+XzOSE1bEtykUPo2RxJqdOt2vlfmoHQ6jUXOlSqXXWGhoZkbspBLFuU7iGliteOYRZ3OQ4USN1wWGw79rtbbXMbjcbPR0wW8rjPWy3h0fMtms+b/lyhb1sW9jndKcmo7yGazWFhqLgan1phm3cMxU8lUz/Pw6uuvAwD20Q5dNwrXr8uaSMdgteScmZlBiO1Wi0XGKV/O5qSNL1DmXSXpmaacJ90SxaOPPsJj5HeXL1/kMbLxnZ0VgqA7IyTAyT/9ljELGByWa7vrsBCxDkm0czTeiHAtOrRjN3R+1Q1VV59sdFf5vLS4Vpjfk1+vgdPoGjnPBxNJVsZjYWFhYWFhYWFhsUlxW5n9LMsR5ykL0V3eyPCQYYzDIbmkFJn+a7SKHGFBKcPel4sIkF3UXWMkKrsr3a1q4ZxyRT67XPdxgxKZVu4WNfk0QMYynxUGRxOLAOA3f/M3AQDptFyT2ki1d0joS62jNPnpzLmzSJHl0YJbGl6+JoQ7kixONcciESvLtGuKRdYUoJAdou50NTSmO+y+oUHzTIIMX33l938fAPDF//DvAQA/9fnPAwCGWcTr4nl5nh0dHdhCFuzkG28AALopXdJkTBPKJjq6u8yzbWlloTLVUFhsOBK0KbsZiw80yHnPa/4eGpKVNdIVtbzkMeslJS6PaUsLKxJ01rxHk1x9ldXIr1UyoAxOdmXVSB80FOHwhL5nyng1HRN2A8gv5XgjyuxTzqMyHqUl9LMMsw9UVLZnEnQDfM1kW0YL6vzsqldGlUyKy/Gm6jWz8s46SZDv+ybipWOV9tP1z3GtnGd9Mq9l9m8N1j/XHM0Ustks5mZvNP0txbFLGUwNofezWGK6tR3fZ/HGDMd4jTxrEp/OXVrcbWVlxSQpLjIRsKdHJA2FolyLSlLLMywU5/hmTM9kJLKlhgc7d0g4XwtMtbAAT1tbGkNk8FdWJargsZ9dHBP2v39AxvHrjJInOV8tLi6iTFvqOttolSYMOUpoZjlHRyLStquVEiJRjcDJvWo/0KToZUY9+ig1unZ5DNGgJmzmeIzcs85pI6MSTS5z3JqZm4PL51fzmm0hLTYe+h3q+kdfVyoV870qe61Jt1rcStu5qhbK5bKRXOlaSF+bfmLUCnJMZ2cnZuebIwcFto1du6TtqwR1ioWrDt9xpzl3itJtbU/K8Jv1IcfowcFBjDMSpvfYkCvLtbjU6M3PSVtdXpbPqJRCeOV73wMA3HeXsPP9XY3iY0DDynx0WO53enbGGKvkWTxrclKupadHjjlF9v4AI3MXxyeQ4nq0k/KqLo5F4xNSrEv7kYlae3UEHS1WSWlPsTmZdz0ss29hYWFhYWFhYWGxSXFbmf2DLDygdkwXLlwAIAyy7tiWyLx3MlE3yp2n7i5Vv5VubTEsszLdyoAr06bHzFEn2DM0iKCxAdTy9bKriyeFsYmEMvwMueaA37jOvfsPAAB+/Mkn5Bo7ZAf3xf/0KwCAFe6+BgcHTeKv2bXyWleX5Fq20joqz3LMISZbDA0NGGbw/EU578iwsPLKRmoybgstoy5eGDM79CB3w1/4whcAAP+AzP7Xv/51AA170nA4jLExSVIeYYlr1ZIeOHCA75FnoqWvHccxO/YPfehDABrRBouNx1VGl9Yni3qe9x6LyEq9Yv4GAJmOtnXHrtGku+sSWbW4libz0gYwiAajrkx7I1FIfoZpa6gMfzQcazDd1DurdN1TS09fmf0QP8NFyFP7TCYJU18ZjkgELEZWVNtkgP3Y9wKAH9Ebk2eiBbPIGtZqwniU6hKpqtZKKEPYliyTD3UkjPF+VHOqetJQKGTuXaNZyh6tL3C29rvRYyyzf2uhY+LEpGjOL14UbW0mk0EoKEylsvNHj0nBRB3vVK+uY/bs3AI8ttE9HBuPnRTDhevMc9Iorke2sFys4Pjx4wAalp43aIXZ19/FY0VXrHNdJBw0kYJYhLplth1j/0d9tPbjyclJpGjhqfl5eSZK6jVpIrLOCfNLcl/RZBIrtP9LMaqhTL/OlQXOR/39osNvScaNNeIU5221B11hvpbmfOm83t3djaU5YYWV0dXEzUucc3Su9krMbUumkGiVe55mLoCNGt866NilunxtK77vG929subarrQtahtV1l4jAUDD+lKP1fVaY61WNb/XMVHXFFV+rp4vz7xHbV9ro9P6U81MKsxBMTp/tq+JiQkEmXNSztK+mfkoa9eSALDCfErNW8mtLiESDTfda7Ug/cQryf2MMNrx8sti8PLEp38CdfYh1ffPzsoxB+66u+nZdDNP7St//E2slmWeSK8r3Kr5K5rgrvNSMhIxeUMBk4gv93czWGbfwsLCwsLCwsLCYpPCseXbLSwsLCwsLCwsLDYnLLNvYWFhYWFhYWFhsUlhF/sWFhYWFhYWFhYWmxR2sW9hYWFhYWFhYWGxSWEX+xYWFhYWFhYWFhabFHaxb2FhYWFhYWFhYbFJYRf7FhYWFhYWFhYWFpsUdrFvYWFhYWFhYWFhsUlhF/sWFhYWFhYWFhYWmxR2sW9hYWFhYWFhYWGxSWEX+xYWFhYWFhYWFhabFHaxb2FhYWFhYWFhYbFJYRf7FhYWFhYWFhYWFpsUdrFvYWFhYWFhYWFhsUlhF/sWFhYWFhYWFhYWmxR2sW9hYWFhYWFhYWGxSWEX+xYWFhYWFhYWFhabFHaxb2FhYWFhYWFhYbFJYRf7FhYWFhYWFhYWFpsUdrFvYWFhYWFhYWFhsUlhF/sWFhYWFhYWFhYWmxR2sW9hYWFhYWFhYWGxSWEX+xYWFhYWFhYWFhabFHaxb2FhYWFhYWFhYbFJ8X8BOxlP1Of1nDUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%local\n",
    "%matplotlib inline\n",
    "base_path = \"hdfs://10.0.104.196:8020/Projects/ImageNet_EndToEnd_MLPipeline/tiny-imagenet/tiny-imagenet-200/train\"\n",
    "sample_images = [\n",
    "    base_path + \"/n01443537/images/n01443537_327.JPEG\",\n",
    "    base_path + \"/n01698640/images/n01698640_381.JPEG\",\n",
    "    base_path + \"/n01882714/images/n01882714_300.JPEG\",\n",
    "    base_path + \"/n01774750/images/n01774750_24.JPEG\",\n",
    "    base_path + \"/n09332890/images/n09332890_300.JPEG\",\n",
    "    base_path + \"/n01443537/images/n01443537_328.JPEG\",\n",
    "    base_path + \"/n01770393/images/n01770393_101.JPEG\",\n",
    "    base_path + \"/n01784675/images/n01784675_101.JPEG\"\n",
    "]\n",
    "img_op_0 = tf.image.decode_jpeg(tf.read_file(sample_images[0]))\n",
    "img_op_1 = tf.image.decode_jpeg(tf.read_file(sample_images[1]))\n",
    "img_op_2 = tf.image.decode_jpeg(tf.read_file(sample_images[2]))\n",
    "img_op_3 = tf.image.decode_jpeg(tf.read_file(sample_images[3]))\n",
    "img_op_4 = tf.image.decode_jpeg(tf.read_file(sample_images[4]))\n",
    "img_op_5 = tf.image.decode_jpeg(tf.read_file(sample_images[5]))\n",
    "img_op_6 = tf.image.decode_jpeg(tf.read_file(sample_images[6]))\n",
    "img_op_7 = tf.image.decode_jpeg(tf.read_file(sample_images[7]))\n",
    "sample_images_parsed = []\n",
    "with tf.Session() as sess:\n",
    "    sample_images_parsed.append(img_op_0.eval())\n",
    "    sample_images_parsed.append(img_op_1.eval())\n",
    "    sample_images_parsed.append(img_op_2.eval())\n",
    "    sample_images_parsed.append(img_op_3.eval())\n",
    "    sample_images_parsed.append(img_op_4.eval())\n",
    "    sample_images_parsed.append(img_op_5.eval())\n",
    "    sample_images_parsed.append(img_op_6.eval())\n",
    "    sample_images_parsed.append(img_op_7.eval())\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (14,10)\n",
    "count = 0\n",
    "for img in sample_images_parsed:\n",
    "    count += 1\n",
    "    plt.subplot(4,4,count)\n",
    "    plt.imshow(img)\n",
    "    plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets look at the class distribution in the train set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%local\n",
    "# this will take 1-2 minutes\n",
    "number_of_examples_per_class = list(map(lambda d: len(hdfs.ls(d + \"/images/\")), hdfs.ls(TRAIN_DIR)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the examples are uniformly distributed over the classes. In the training dataset there are 200 classes with 500 examples in each (total 100 000 images in the training dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%local\n",
    "plt.hist(number_of_examples_per_class, bins='auto')\n",
    "plt.xlabel('Number of examples)')\n",
    "plt.ylabel('Number of classes')\n",
    "plt.title('Class distribution histogram')\n",
    "plt.savefig(\"class_distribution.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parse Metadata about the Dataset\n",
    "\n",
    "The dataset has some .txt files with annotation and other metadata that needs to be parsed. This metadata for example explains what the label for the image directories are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_metadata():\n",
    "    \"\"\" \n",
    "    Parses the words.txt file into a map of label -> words and a list of ordered nids (index of nid = integer label).\n",
    "    Also parses the val_annotations.txt file into a map of (validation_file_name --> nid)\n",
    "    \"\"\"\n",
    "    # list all directories in the train set, the directory name is the \"nid\" and identifies the label\n",
    "    train_dirs = py_hdfs.ls(TRAIN_DIR)\n",
    "    \n",
    "    # remove the path except the nid\n",
    "    train_nid_list = list(map(lambda x: x.replace(TRAIN_DIR + \"/\", \"\"), train_dirs))\n",
    "    \n",
    "    # the number of nids equal the number of unique classes/labels\n",
    "    num_classes = len(train_nid_list)\n",
    "    \n",
    "    # read the words.txt file that contains lines of the form \"nid\\words\"\n",
    "    with py_hdfs.open(ID_TO_CLASS_FILE, 'r') as f:\n",
    "        file_lines = f.read().decode(\"utf-8\").split(\"\\n\")\n",
    "    label_to_word = {}\n",
    "    \n",
    "    for l in file_lines:\n",
    "        # parse each line\n",
    "        wnid, word = l.split('\\t')\n",
    "        if wnid in train_nid_list:\n",
    "            # convert the nids into integer labels by using the position in the index\n",
    "            label = train_nid_list.index(wnid)\n",
    "            word = str(label) + \": \" + word\n",
    "            # save the mapping of integer label --> words\n",
    "            label_to_word[label] = word\n",
    "    \n",
    "    # read the val_annotations.txt file that contains lines of the form: \n",
    "    # \"validation_image\\tnid\\tx_pos\\ty_pos\\tw_pos\\th_pos\"\n",
    "    with py_hdfs.open(VAL_LABELS_FILE, 'r') as f:\n",
    "        file_lines = f.read().decode(\"utf-8\").split(\"\\n\")\n",
    "    validation_file_to_nid = {}\n",
    "    for l in file_lines:\n",
    "        # parse each line\n",
    "        tokens = l.split('\\t')\n",
    "        #skip corrupted lines\n",
    "        if len(tokens) > 2:\n",
    "            validation_img = tokens[0]\n",
    "            wnid = tokens[1]\n",
    "            # we only care about classification in this tutorial, not localization \n",
    "            if wnid in train_nid_list:\n",
    "                validation_file_to_nid[validation_img] = wnid\n",
    "    \n",
    "    return train_nid_list, label_to_word, validation_file_to_nid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insert Training Data to the Feature Store as a Feature Group\n",
    "\n",
    "The training data is split into several directories, we need to join them together before we can put them in the featurestore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def filter_shapes(row):\n",
    "    \"\"\"\n",
    "    Filter to remove images of wrong shape. \n",
    "    2% of the tinyimagenet images are not colored, while the rest are, this filter will simply drop\n",
    "    the non-colored images.\n",
    "    \n",
    "    Args:\n",
    "          :row: a row from a spark dataframe containing images\n",
    "    \n",
    "    Returns:\n",
    "           True if the image matches the expected dimensions, False otherwise\n",
    "          \n",
    "    \"\"\"\n",
    "    try:\n",
    "        image = row.image\n",
    "        height = image.height\n",
    "        width = image.width\n",
    "        nChannels = image.nChannels\n",
    "        if height != HEIGHT or width != WIDTH or nChannels != CHANNELS:\n",
    "            return False\n",
    "        else:\n",
    "            return True\n",
    "    except:\n",
    "        return False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def insert_train_dirs_into_featurestore():\n",
    "    \"\"\"\n",
    "    Loops over all directories with training data and inserts them into the featurestore.\n",
    "    There are 500 images in each directory, the name of the directory indicates the label of the images.\n",
    "    \n",
    "    Inserts the training data into the featurestore as a feature group (Hive table) with schema:\n",
    "    \n",
    "    |image|label|\n",
    "    \"\"\"\n",
    "    # Make sure feature store metadata is up to date\n",
    "    featurestore.get_featurestore_metadata(update_cache=True)\n",
    "    # Parse TinyImageNet metadata file from HopsFS\n",
    "    nid_list, label_to_word, validation_file_to_nid = parse_metadata()\n",
    "    # Read the train dir from HopsFS into a spark image dataframe\n",
    "    train_dir = TRAIN_DIR + \"/*/images/\"\n",
    "    image_df = spark.read.format(\"image\").load(train_dir)\n",
    "    # Filter out images with wrong dimensions\n",
    "    image_df_filtered = image_df.rdd.filter(filter_shapes).toDF()\n",
    "    # Add the label to each image file (label can be inferred using the nid_list and the image file name)\n",
    "    def map_label(row):\n",
    "        Example = Row(\"image\", \"label\")\n",
    "        filename = row.image.origin\n",
    "        # Extract label from the file name and the nid_list\n",
    "        label = nid_list.index(re.sub(\"[^images]*$\", \"\", filename.replace(TRAIN_DIR, \"\").replace(\"/\", \"\")).replace(\"images\",\"\"))\n",
    "        return Example(row.image, label)\n",
    "    features_df = image_df_filtered.rdd.map(map_label).toDF()\n",
    "    featurestore.create_featuregroup(features_df, TRAIN_FEATUREGROUP, \n",
    "                                     description=\"train data for image classification with TinyImageNet, contains labelled train images\",\n",
    "                                     feature_correlation=False, \n",
    "                                     cluster_analysis=False,\n",
    "                                    feature_histograms=False,\n",
    "                                    descriptive_statistics=True,\n",
    "                                    featuregroup_version=TRAIN_FEATUREGROUP_VERSION\n",
    "                                    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insert Testing Data to the Feature Store as a Feature Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def insert_test_dir_into_featurestore():\n",
    "    \"\"\"\n",
    "    Reads a single directory of images from HopsFS and inserts it into \n",
    "    the featurestore as a feature group (Hive table) with schema:\n",
    "    \n",
    "    |image|\n",
    "    \"\"\"\n",
    "    test_dir = TEST_DIR + \"/images/\"\n",
    "    # Read the train dir from HopsFS into a spark image dataframe\n",
    "    image_df = spark.read.format(\"image\").load(test_dir)\n",
    "    # Filter out images with wrong dimensions\n",
    "    image_df_filtered = image_df.rdd.filter(filter_shapes).toDF()\n",
    "    # Insert into feature store as a feature group\n",
    "    featurestore.create_featuregroup(image_df_filtered, TEST_FEATUREGROUP, \n",
    "                                     description = \"test data for image classification with TinyImageNet, contains unlabelled test images\",\n",
    "                                     feature_correlation=False, \n",
    "                                     cluster_analysis=False, feature_histograms=False,\n",
    "                                     descriptive_statistics=True, \n",
    "                                     featuregroup_version=TEST_FEATUREGROUP_VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Insert Validation Data to the Feature Store as a Feature Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def insert_val_dir_into_featurestore():\n",
    "    \"\"\"\n",
    "    Reads a single directory of images from HopsFS and inserts it into \n",
    "    the featurestore as a feature group (Hive table) with schema:\n",
    "    \n",
    "    |image|label|\n",
    "    \"\"\"\n",
    "    # Parse TinyImageNet metadata file from HopsFS\n",
    "    nid_list, label_to_word, validation_file_to_nid = parse_metadata()\n",
    "    # Read the train dir from HopsFS into a spark image dataframe\n",
    "    val_dir = VAL_DIR\n",
    "    image_df = spark.read.format(\"image\").load(val_dir)\n",
    "    # Filter out images with wrong dimensions\n",
    "    image_df_filtered = image_df.rdd.filter(filter_shapes).toDF()\n",
    "    # Add the label to each image file (label can be inferred using the nid_list and the image file name)\n",
    "    def map_label(row):\n",
    "        Example = Row(\"image\", \"label\")\n",
    "        filename = row.image.origin\n",
    "        label = nid_list.index(validation_file_to_nid[filename.replace(VAL_DIR, \"\")])\n",
    "        return Example(row.image, label)\n",
    "    features_df = image_df_filtered.rdd.map(map_label).toDF()\n",
    "    # Insert into feature store as a feature group\n",
    "    featurestore.create_featuregroup(features_df, VAL_FEATUREGROUP, \n",
    "                                     description = \"validation data for image classification with TinyImageNet, contains labelled images\",\n",
    "                                     feature_correlation=False, \n",
    "                                     cluster_analysis=False, feature_histograms=False,\n",
    "                                     descriptive_statistics=True, \n",
    "                                     featuregroup_version=VAL_FEATUREGROUP_VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run The Pipeline\n",
    "\n",
    "1. Insert training data in the feature store as a feature group\n",
    "2. Insert test data in the feature store as a feature group\n",
    "3. Insert validation data in the feature store as a feature group\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing descriptive statistics for : train_tinyimagenet\n",
      "Running sql: use endtoendv2_featurestore\n",
      "Feature group created successfully"
     ]
    }
   ],
   "source": [
    "insert_train_dirs_into_featurestore()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing descriptive statistics for : test_tinyimagenet\n",
      "Running sql: use endtoendv2_featurestore\n",
      "Feature group created successfully"
     ]
    }
   ],
   "source": [
    "insert_test_dir_into_featurestore()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "computing descriptive statistics for : val_tinyimagenet\n",
      "Running sql: use endtoendv2_featurestore\n",
      "Feature group created successfully"
     ]
    }
   ],
   "source": [
    "insert_val_dir_into_featurestore()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have three feature groups in the feature store stored in ORC rather than a directory with tons of JPEGs and attached metadata. \n",
    "\n",
    "- train_tinyimagenet\n",
    "- test_tinyimagenet\n",
    "- val_tinyimagenet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Verify The Results\n",
    "\n",
    "TinyImageNet dataset contains a training set of 100,000 images, a validation set of 10,000 images, and a test set of also 10,000 images. These images are sourced from 200 different classes of objects. The images are downscaled from the original ImageNet’s dataset size of 256x256 to 64x64. \n",
    "\n",
    "2% of the images are grey-scaled and the rest are colored. We have filtered out the grey scaled images, so the counts should be slightly lower than 100,000, 10,000, and 10,000. The dimensions should be 64x64x3 since the images are colored."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sql: use endtoendv2_featurestore\n",
      "Running sql: SELECT * FROM train_tinyimagenet_1\n",
      "98179"
     ]
    }
   ],
   "source": [
    "featurestore.get_featuregroup(TRAIN_FEATUREGROUP).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sql: use endtoendv2_featurestore\n",
      "Running sql: SELECT * FROM val_tinyimagenet_1\n",
      "9832"
     ]
    }
   ],
   "source": [
    "featurestore.get_featuregroup(VAL_FEATUREGROUP).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sql: use endtoendv2_featurestore\n",
      "Running sql: SELECT * FROM test_tinyimagenet_1\n",
      "9811"
     ]
    }
   ],
   "source": [
    "featurestore.get_featuregroup(TEST_FEATUREGROUP).count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Training Datasets\n",
    "\n",
    "We can now take the feature groups in the feature store and convert them into training datasets in a suitable format. TFRecords is the recommended format when using Tensorflow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_np_array(image):\n",
    "    \"\"\"\n",
    "    Converts a spark image representation to a numpy array\n",
    "    \n",
    "    Args:\n",
    "        :image: the image to convert\n",
    "    \n",
    "    Returns:\n",
    "            A multi-dimensional numpy array representing the image\n",
    "    \"\"\"\n",
    "    height = image.height\n",
    "    width = image.width\n",
    "    nChannels = image.nChannels\n",
    "    return np.ndarray(\n",
    "        shape=(height, width, nChannels),\n",
    "        dtype=np.uint8,\n",
    "        buffer=image.data,\n",
    "        strides=(width * nChannels, nChannels, 1)).astype(np.int64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_train_dataset():\n",
    "    \"\"\"\n",
    "    Creates a tfrecords dataset in the feature store, containing the train images and labels\n",
    "    \"\"\"\n",
    "    # Read images from feature store (ORC)\n",
    "    feature_df = featurestore.get_featuregroup(TRAIN_FEATUREGROUP)\n",
    "    # Convert the image representation to a flattened float array\n",
    "    def mapper(row):\n",
    "        Example = Row(\"image\", \"label\")\n",
    "        label = row.label # long label\n",
    "        image = to_np_array(row.image).flatten().tolist() #12288 length array with the image data (64x64x3)\n",
    "        return Example(image, label)\n",
    "    feature_df2 = feature_df.rdd.filter(filter_shapes).map(mapper).toDF()\n",
    "    # Save as a training dataset in TFRecords format in the feature store\n",
    "    featurestore.create_training_dataset(feature_df2, \n",
    "                                         TRAIN_DATASET,\n",
    "                                         description=\"training dataset for TinyImageNet Classification, Stored as TFRecords\",\n",
    "                                        data_format=\"tfrecords\",\n",
    "                                        descriptive_statistics=True,\n",
    "                                        training_dataset_version=1,\n",
    "                                        feature_correlation=False, \n",
    "                                        cluster_analysis=False, \n",
    "                                        feature_histograms=False,\n",
    "                                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_test_dataset():\n",
    "    \"\"\"\n",
    "    Creates a tfrecords dataset in the feature store, containing the test images\n",
    "    \"\"\"    \n",
    "    # Read images from feature store (ORC)\n",
    "    feature_df = featurestore.get_featuregroup(TEST_FEATUREGROUP)\n",
    "    # Convert the image representation to a flattened float array\n",
    "    def mapper(row):\n",
    "        Example = Row(\"image\")\n",
    "        image = to_np_array(row.image).flatten().tolist() #12288 length array with the image data (64x64x3)\n",
    "        return Example(image)\n",
    "    feature_df2 = feature_df.rdd.filter(filter_shapes).map(mapper).toDF()\n",
    "    # Save as a dataset in TFRecords format in the feature store\n",
    "    featurestore.create_training_dataset(feature_df2, \n",
    "                                         TEST_DATASET,\n",
    "                                         description=\"test dataset for TinyImageNet Classification, Stored as TFRecords\",\n",
    "                                        data_format=\"tfrecords\",\n",
    "                                        descriptive_statistics=True,\n",
    "                                        training_dataset_version=1,\n",
    "                                        feature_correlation=False, \n",
    "                                        cluster_analysis=False, \n",
    "                                        feature_histograms=False,\n",
    "                                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_val_dataset():\n",
    "    \"\"\"\n",
    "    Creates a tfrecords dataset in the feature store, containing the validation images and labels\n",
    "    \"\"\"\n",
    "    # Read images from feature store (ORC)\n",
    "    feature_df = featurestore.get_featuregroup(VAL_FEATUREGROUP)\n",
    "    # Convert the image representation to a flattened float array\n",
    "    def mapper(row):\n",
    "        Example = Row(\"image\", \"label\")\n",
    "        label = row.label # long label\n",
    "        image = to_np_array(row.image).flatten().tolist() #12288 length array with the image data (64x64x3)\n",
    "        return Example(image, label)\n",
    "    feature_df2 = feature_df.rdd.filter(filter_shapes).map(mapper).toDF()\n",
    "    # Save as a training dataset in TFRecords format in the feature store\n",
    "    featurestore.create_training_dataset(feature_df2, \n",
    "                                         VAL_DATASET,\n",
    "                                         description=\"validation dataset for TinyImageNet Classification, Stored as TFRecords\",\n",
    "                                        data_format=\"tfrecords\",\n",
    "                                        descriptive_statistics=True,\n",
    "                                        training_dataset_version=1,\n",
    "                                        feature_correlation=False, \n",
    "                                        cluster_analysis=False, \n",
    "                                        feature_histograms=False,\n",
    "                                        )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the Pipeline\n",
    "\n",
    "1. Insert training data in the feature store as a training dataset\n",
    "2. Insert test data in the feature store as a training dataset\n",
    "3. Insert validation data in the feature store as a training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sql: use endtoendv2_featurestore\n",
      "Running sql: SELECT * FROM train_tinyimagenet_1\n",
      "computing descriptive statistics for : train_dataset_tinyimagenet\n",
      "Training Dataset created successfully"
     ]
    }
   ],
   "source": [
    "create_train_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sql: use endtoendv2_featurestore\n",
      "Running sql: SELECT * FROM test_tinyimagenet_1\n",
      "computing descriptive statistics for : test_dataset_tinyimagenet\n",
      "Training Dataset created successfully"
     ]
    }
   ],
   "source": [
    "create_test_dataset()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running sql: use endtoendv2_featurestore\n",
      "Running sql: SELECT * FROM val_tinyimagenet_1\n",
      "computing descriptive statistics for : val_dataset_tinyimagenet\n",
      "Training Dataset created successfully"
     ]
    }
   ],
   "source": [
    "create_val_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect Training Dataset\n",
    "\n",
    "We can read the tfrecords from disk and very that we stored the data correctly and that the labels match. Once again, to do plotting we have to use %%local so that it will run the computation on the local machine where we have a display, rather than remotely in the spark driver in the cluster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'hdfs://10.0.104.196:8020/Projects/EndToEndV2/EndToEndV2_Training_Datasets/train_dataset_tinyimagenet_1/train_dataset_tinyimagenet'"
     ]
    }
   ],
   "source": [
    "featurestore.get_training_dataset_path(TRAIN_DATASET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'image': FixedLenFeature(shape=[12288], dtype=tf.int64, default_value=None), 'label': FixedLenFeature(shape=[], dtype=tf.int64, default_value=None)}"
     ]
    }
   ],
   "source": [
    "featurestore.get_training_dataset_tf_record_schema(TRAIN_DATASET)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%local\n",
    "%matplotlib inline\n",
    "import pydoop.hdfs as py_hdfs\n",
    "# We have to redfine this function so that we can use it in the %%local environment\n",
    "def parse_metadata():\n",
    "    \"\"\" \n",
    "    Parses the words.txt file into a map of label -> words and a list of ordered nids (index of nid = integer label).\n",
    "    Also parses the val_annotations.txt file into a map of (validation_file_name --> nid)\n",
    "    \"\"\"\n",
    "    # list all directories in the train set, the directory name is the \"nid\" and identifies the label\n",
    "    train_dirs = py_hdfs.ls(TRAIN_DIR)\n",
    "    \n",
    "    # remove the path except the nid\n",
    "    train_nid_list = list(map(lambda x: x.replace(TRAIN_DIR + \"/\", \"\"), train_dirs))\n",
    "    \n",
    "    # the number of nids equal then number of unique classes/labels\n",
    "    num_classes = len(train_nid_list)\n",
    "    \n",
    "    # read the words.txt file that contains lines of the form \"nid\\twords\"\n",
    "    with py_hdfs.open(ID_TO_CLASS_FILE, 'r') as f:\n",
    "        file_lines = f.read().decode(\"utf-8\").split(\"\\n\")\n",
    "    label_to_word = {}\n",
    "    \n",
    "    for l in file_lines:\n",
    "        # parse each line\n",
    "        wnid, word = l.split('\\t')\n",
    "        if wnid in train_nid_list:\n",
    "            # convert the nids into integer labels by using the position in the index\n",
    "            label = train_nid_list.index(wnid)\n",
    "            word = str(label) + \": \" + word\n",
    "            # save the mapping of integer label --> words\n",
    "            label_to_word[label] = word\n",
    "    \n",
    "    # read the val_annotations.txt file that contains lines of the form: \n",
    "    # \"validation_image\\tnid\\tx_pos\\ty_pos\\tw_pos\\th_pos\"\n",
    "    with py_hdfs.open(VAL_LABELS_FILE, 'r') as f:\n",
    "        file_lines = f.read().decode(\"utf-8\").split(\"\\n\")\n",
    "    validation_file_to_nid = {}\n",
    "    for l in file_lines:\n",
    "        # parse each line\n",
    "        tokens = l.split('\\t')\n",
    "        #skip corrupted lines\n",
    "        if len(tokens) > 2:\n",
    "            validation_img = tokens[0]\n",
    "            wnid = tokens[1]\n",
    "            # we only care about classification in this tutorial, not localization \n",
    "            if wnid in train_nid_list:\n",
    "                validation_file_to_nid[validation_img] = wnid\n",
    "    \n",
    "    return train_nid_list, label_to_word, validation_file_to_nid\n",
    "\n",
    "def create_tf_dataset():\n",
    "    \"\"\"\n",
    "    Creates a Tensorflow Dataset from TFRecords on HopsFS stored in the feature store\n",
    "    \"\"\"\n",
    "    dataset_dir = 'hdfs://10.0.104.196:8020/Projects/EndToEndV2/EndToEndV2_Training_Datasets/train_dataset_tinyimagenet_1/train_dataset_tinyimagenet'\n",
    "    input_files = tf.gfile.Glob(dataset_dir + \"/part-r-*\")\n",
    "    dataset = tf.data.TFRecordDataset(input_files)\n",
    "    tf_record_schema = {\n",
    "        'image': tf.FixedLenFeature(shape=[12288], dtype=tf.int64, default_value=None),\n",
    "        'label': tf.FixedLenFeature(shape=[], dtype=tf.int64, default_value=None)\n",
    "    }\n",
    "\n",
    "    def decode(example_proto):\n",
    "        example = tf.parse_single_example(example_proto, tf_record_schema)\n",
    "        label = example[\"label\"]\n",
    "        image_flat = example[\"image\"]\n",
    "        image = tf.reshape(image_flat, (64,64,3))\n",
    "        return image, label\n",
    "\n",
    "    dataset = dataset.map(decode).shuffle(100)\n",
    "    return dataset\n",
    "\n",
    "# Read 8 images from HopsFS and decode the protobufs\n",
    "sample_images_parsed = []\n",
    "sample_labels_parsed = []\n",
    "train_nid_list, label_to_word, validation_file_to_nid = parse_metadata() \n",
    "with tf.Session() as sess:\n",
    "    dataset = create_tf_dataset()\n",
    "    dataset_iter = dataset.make_one_shot_iterator()\n",
    "    for i in range(8):\n",
    "        x,y = sess.run(dataset_iter.get_next())\n",
    "        sample_images_parsed.append(x)\n",
    "        sample_labels_parsed.append(y)\n",
    "\n",
    "# Plot the parsed images        \n",
    "plt.rcParams[\"figure.figsize\"] = (14,10)\n",
    "count = 0\n",
    "for i in range(len(sample_images_parsed)):\n",
    "    count += 1\n",
    "    plt.subplot(4,4,count)\n",
    "    plt.title('label: {}'.format(label_to_word[sample_labels_parsed[i]]))\n",
    "    plt.imshow(sample_images_parsed[i])\n",
    "    plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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